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Prof. Jordi Solé-Casals received the M.Sc. degree in Telecommunication Engineering in 1995, and the Ph. D. degree with European label in 2000, both from the Polytechnic University of Catalonia (UPC), Barcelona, and the Bachelor degree of Humanities from the Open University of Catalonia (UOC), Barcelona, in 2010. In 1994 he joined the Department of Engineering at the University of Vic - Central University of Catalonia. He is the Head of the Data and Signal Processing Research Group and maintains active collaborations with other international groups. He was Visiting Researcher within the Gipsa-Lab in Grenoble (France), LABSP-RIKEN in Tokyo (Japan), BMU in Cambridge (United Kingdom) and Tensor Learning Unit (TLU, RIKEN AIP) in Tokyo (Japan). He is currently Visiting Researcher at the Department of Psychiatry of the University of Cambridge and Visiting Researcher at the College of Artificial Intelligence, Nankai University, China. His research interests are in neurosciences, biomedical signal processing, machine learning/deep learning, neural networks, source separation and biometrics.
Gifted children are able to learn in a more advanced way than others, probably due to neurophysiological differences in the communication efficiency in neural pathways. Topological features contribute to understanding the correlation between the brain structure and intelligence. Despite decades of neuroscience research using MRI, methods based on brain region connectivity patterns are limited by MRI artifacts, which therefore leads to revisiting MRI morphometric features, with the aim of using them to directly identify gifted children instead of using brain connectivity. However, the small, high-dimensional morphometric feature dataset with outliers makes the task of finding good classification models challenging. To this end, a hybrid method is proposed that combines tensor completion and feature selection methods to handle outliers and then select the discriminative features. The proposed method can achieve a classification accuracy of 93.1%, higher than other existing algorithms, which is thus suitable for the small MRI datasets with outliers in supervised classification scenarios.
Jin Zhang; Fan Feng; Tianyi Han; Feng Duan; Zhe Sun; F. Cesar Caiafa; Jordi Solé-Casals. A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification. Science China Technological Sciences 2021, 1 -9.
AMA StyleJin Zhang, Fan Feng, Tianyi Han, Feng Duan, Zhe Sun, F. Cesar Caiafa, Jordi Solé-Casals. A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification. Science China Technological Sciences. 2021; ():1-9.
Chicago/Turabian StyleJin Zhang; Fan Feng; Tianyi Han; Feng Duan; Zhe Sun; F. Cesar Caiafa; Jordi Solé-Casals. 2021. "A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification." Science China Technological Sciences , no. : 1-9.
Today, the use of SCADA data for predictive maintenance and forecasting of wind turbines in wind farms is gaining popularity due to the low cost of this solution compared to others that require the installation of additional equipment. SCADA data provides four statistical measures (mean, standard deviation, maximum value, and minimum value) of hundreds of wind turbine magnitudes, usually in a 5-min or 10-min interval. Several studies have analysed the loss of information associated with the reduction of information when using five minutes instead of four seconds as a sampling frequency, or when compressing a time series recorded at 5 min to 10 min, concluding that some, but not all, of these magnitudes are seriously affected. However, to our knowledge, there are no studies on increasing the time interval beyond 10 min to take these four statistical values, and how this aggregation affects prognosis models. Our work shows that, despite the irreversible loss of information that occurs in the first 5 min, increasing the time considered to take the four representative statistical values improves the performance of the predicted targets in normality models.
Pere Marti-Puig; Alejandro Bennásar-Sevillá; Alejandro Blanco-M.; Jordi Solé-Casals. Exploring the Effect of Temporal Aggregation on SCADA Data for Wind Turbine Prognosis Using a Normality Model. Applied Sciences 2021, 11, 6405 .
AMA StylePere Marti-Puig, Alejandro Bennásar-Sevillá, Alejandro Blanco-M., Jordi Solé-Casals. Exploring the Effect of Temporal Aggregation on SCADA Data for Wind Turbine Prognosis Using a Normality Model. Applied Sciences. 2021; 11 (14):6405.
Chicago/Turabian StylePere Marti-Puig; Alejandro Bennásar-Sevillá; Alejandro Blanco-M.; Jordi Solé-Casals. 2021. "Exploring the Effect of Temporal Aggregation on SCADA Data for Wind Turbine Prognosis Using a Normality Model." Applied Sciences 11, no. 14: 6405.
Essential tremor (ET) is a highly prevalent neurological disorder characterized by action-induced tremors involving the hand, voice, head, and/or face. Importantly, hand tremor is present in nearly all forms of ET, resulting in impaired fine motor skills and diminished quality of life. To advance early diagnostic approaches for ET, automated handwriting tasks and magnetic resonance imaging (MRI) offer an opportunity to develop early essential clinical biomarkers. In this study, we present a novel approach for the early clinical diagnosis and monitoring of ET based on integrating handwriting and neuroimaging analysis. We demonstrate how the analysis of fine motor skills, as measured by an automated Archimedes’ spiral task, is correlated with neuroimaging biomarkers for ET. Together, we present a novel modeling approach that can serve as a complementary and promising support tool for the clinical diagnosis of ET and a large range of tremors.
Karmele Lopez-De-Ipina; Jordi Solé-Casals; José Ignacio Sánchez-Méndez; Rafael Romero-Garcia; Elsa Fernandez; Catalina Requejo; Anujan Poologaindran; Marcos Faúndez-Zanuy; José Félix Martí-Massó; Alberto Bergareche; John Suckling. Analysis of Fine Motor Skills in Essential Tremor: Combining Neuroimaging and Handwriting Biomarkers for Early Management. Frontiers in Human Neuroscience 2021, 15, 1 .
AMA StyleKarmele Lopez-De-Ipina, Jordi Solé-Casals, José Ignacio Sánchez-Méndez, Rafael Romero-Garcia, Elsa Fernandez, Catalina Requejo, Anujan Poologaindran, Marcos Faúndez-Zanuy, José Félix Martí-Massó, Alberto Bergareche, John Suckling. Analysis of Fine Motor Skills in Essential Tremor: Combining Neuroimaging and Handwriting Biomarkers for Early Management. Frontiers in Human Neuroscience. 2021; 15 ():1.
Chicago/Turabian StyleKarmele Lopez-De-Ipina; Jordi Solé-Casals; José Ignacio Sánchez-Méndez; Rafael Romero-Garcia; Elsa Fernandez; Catalina Requejo; Anujan Poologaindran; Marcos Faúndez-Zanuy; José Félix Martí-Massó; Alberto Bergareche; John Suckling. 2021. "Analysis of Fine Motor Skills in Essential Tremor: Combining Neuroimaging and Handwriting Biomarkers for Early Management." Frontiers in Human Neuroscience 15, no. : 1.
The objectives of this study were to determine the amplitude of movement differences and asymmetries between feet during the stance phase and to evaluate the effects of foot orthoses (FOs) on foot kinematics in the stance phase during running. In total, 40 males were recruited (age: 43.0 ± 13.8 years, weight: 72.0 ± 5.5 kg, height: 175.5 ± 7.0 cm). Participants ran on a running treadmill at 2.5 m/s using their own footwear, with and without the FOs. Two inertial sensors fixed on the instep of each of the participant’s footwear were used. Amplitude of movement along each axis, contact time and number of steps were considered in the analysis. The results indicate that the movement in the sagittal plane is symmetric, but that it is not in the frontal and transverse planes. The right foot displayed more degrees of movement amplitude than the left foot although these differences are only significant in the abduction case. When FOs are used, a decrease in amplitude of movement in the three axes is observed, except for the dorsi-plantar flexion in the left foot and both feet combined. The contact time and the total step time show a significant increase when FOs are used, but the number of steps is not altered, suggesting that FOs do not interfere in running technique. The reduction in the amplitude of movement would indicate that FOs could be used as a preventive tool. The FOs do not influence the asymmetry of the amplitude of movement observed between feet, and this risk factor is maintained. IMU devices are useful tools to detect risk factors related to running injuries. With its use, even more personalized FOs could be manufactured.
Juan Florenciano Restoy; Jordi Solé-Casals; Xantal Borràs-Boix. IMU-Based Effects Assessment of the Use of Foot Orthoses in the Stance Phase during Running and Asymmetry between Extremities. Sensors 2021, 21, 3277 .
AMA StyleJuan Florenciano Restoy, Jordi Solé-Casals, Xantal Borràs-Boix. IMU-Based Effects Assessment of the Use of Foot Orthoses in the Stance Phase during Running and Asymmetry between Extremities. Sensors. 2021; 21 (9):3277.
Chicago/Turabian StyleJuan Florenciano Restoy; Jordi Solé-Casals; Xantal Borràs-Boix. 2021. "IMU-Based Effects Assessment of the Use of Foot Orthoses in the Stance Phase during Running and Asymmetry between Extremities." Sensors 21, no. 9: 3277.
In this article, we present a collection of fifteen novel contributions on machine learning methods with low-quality or imperfect datasets, which were accepted for publication in the special issue “Machine Learning Methods with Noisy, Incomplete or Small Datasets”, Applied Sciences (ISSN 2076-3417). These papers provide a variety of novel approaches to real-world machine learning problems where available datasets suffer from imperfections such as missing values, noise or artefacts. Contributions in applied sciences include medical applications, epidemic management tools, methodological work, and industrial applications, among others. We believe that this special issue will bring new ideas for solving this challenging problem, and will provide clear examples of application in real-world scenarios.
Cesar Caiafa; Zhe Sun; Toshihisa Tanaka; Pere Marti-Puig; Jordi Solé-Casals. Machine Learning Methods with Noisy, Incomplete or Small Datasets. Applied Sciences 2021, 11, 4132 .
AMA StyleCesar Caiafa, Zhe Sun, Toshihisa Tanaka, Pere Marti-Puig, Jordi Solé-Casals. Machine Learning Methods with Noisy, Incomplete or Small Datasets. Applied Sciences. 2021; 11 (9):4132.
Chicago/Turabian StyleCesar Caiafa; Zhe Sun; Toshihisa Tanaka; Pere Marti-Puig; Jordi Solé-Casals. 2021. "Machine Learning Methods with Noisy, Incomplete or Small Datasets." Applied Sciences 11, no. 9: 4132.
In this paper, a method to build models to monitor and evaluate the health status of wind turbines using Single-hidden Layer Feedforward Neural networks (SLFN) is presented. The models are trained using the Extreme Learning Machines (ELM) strategy. The data used is obtained from the SCADA systems, easily available in modern wind turbines. The ELM technique requires very low computational costs for the training of the models, and thus allows for the integration of a grid-search approach with parallelized instances to find out the optimal model parameters. These models can be built both individually, considering the turbines separately, or as an aggregate for the whole wind plant. The followed strategy consists in predicting a target variable using the rest of the variables of the system/subsystem, computing the error deviation from the real target variable and finally comparing high error values with a selection of alarm events for that system, therefore validating the performance of the model. The experimental results indicate that this methodology leads to the detection of mismatches in the stages of the system’s failure, thus making it possible to schedule the maintenance operation before a critical failure occurs. The simplicity of the ELM systems and the ease with which the parameters can be adjusted make it a realistic option to be implemented in wind turbine models to work in real time.
Pere Marti-Puig; Alejandro Blanco-M.; Moisès Serra-Serra; Jordi Solé-Casals. Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines. Applied Sciences 2021, 11, 590 .
AMA StylePere Marti-Puig, Alejandro Blanco-M., Moisès Serra-Serra, Jordi Solé-Casals. Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines. Applied Sciences. 2021; 11 (2):590.
Chicago/Turabian StylePere Marti-Puig; Alejandro Blanco-M.; Moisès Serra-Serra; Jordi Solé-Casals. 2021. "Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines." Applied Sciences 11, no. 2: 590.
Electroencephalography (EEG) is a non-invasive technology used for the human brain-computer interface. One of its important applications is the evaluation of the mental state of an individual, such as workload estimation. In previous works, common spatial pattern feature extraction methods have been proposed for the EEG-based workload detection. Recently, several novel methods were introduced to detect EEG pattern workloads. However, it is still unknown which one of these methods is the one that offers the best performance for the workload EEG pattern feature detection. In this paper, four methods were used to extract workload EEG features: (a) common spatial pattern feature extraction; (b) temporally constrained sparse group spatial pattern feature extraction; (c) EEGnet; and (d) the new proposed shallow convolutional neural network for workload estimation (WLnet). The classification accuracy of these four methods was compared. Experimental results demonstrate that the proposed WLnet achieved the best detection accuracy in both stress and non-stress conditions. We believe that the proposed methods may be relevant to real-life applications of mental workload estimation.
Zhe Sun; Binghua Li; Feng Duan; Hao Jia; Shan Wang; Yu Liu; Andrzej Cichocki; Cesar F. Caiafa; Jordi Sole-Casals. WLnet: Towards an Approach for Robust Workload Estimation Based on Shallow Neural Networks. IEEE Access 2020, 9, 3165 -3173.
AMA StyleZhe Sun, Binghua Li, Feng Duan, Hao Jia, Shan Wang, Yu Liu, Andrzej Cichocki, Cesar F. Caiafa, Jordi Sole-Casals. WLnet: Towards an Approach for Robust Workload Estimation Based on Shallow Neural Networks. IEEE Access. 2020; 9 (99):3165-3173.
Chicago/Turabian StyleZhe Sun; Binghua Li; Feng Duan; Hao Jia; Shan Wang; Yu Liu; Andrzej Cichocki; Cesar F. Caiafa; Jordi Sole-Casals. 2020. "WLnet: Towards an Approach for Robust Workload Estimation Based on Shallow Neural Networks." IEEE Access 9, no. 99: 3165-3173.
In many machine learning applications, measurements are sometimes incomplete or noisy resulting in missing features. In other cases, and for different reasons, the datasets are originally small, and therefore, more data samples are required to derive useful supervised or unsupervised classification methods. Correct handling of incomplete, noisy or small datasets in machine learning is a fundamental and classic challenge. In this article, we provide a unified review of recently proposed methods based on signal decomposition for missing features imputation (data completion), classification of noisy samples and artificial generation of new data samples (data augmentation). We illustrate the application of these signal decomposition methods in diverse selected practical machine learning examples including: brain computer interface, epileptic intracranial electroencephalogram signals classification, face recognition/verification and water networks data analysis. We show that a signal decomposition approach can provide valuable tools to improve machine learning performance with low quality datasets.
Cesar Federico Caiafa; Jordi Solé-Casals; Pere Marti-Puig; Sun Zhe; Toshihisa Tanaka. Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets. Applied Sciences 2020, 10, 8481 .
AMA StyleCesar Federico Caiafa, Jordi Solé-Casals, Pere Marti-Puig, Sun Zhe, Toshihisa Tanaka. Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets. Applied Sciences. 2020; 10 (23):8481.
Chicago/Turabian StyleCesar Federico Caiafa; Jordi Solé-Casals; Pere Marti-Puig; Sun Zhe; Toshihisa Tanaka. 2020. "Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets." Applied Sciences 10, no. 23: 8481.
Previous studies made progress in the early diagnosis of Alzheimer’s disease (AD) using electroencephalography (EEG) without considering EEG connectivity. To fill this gap, we explored significant differences between early AD patients and controls based on frequency domain and spatial properties using functional connectivity in mild cognitive impairment (MCI) and mild AD datasets. Four global metrics, network resilience, connection-level metrics and node versatility were used to distinguish between controls and patients. The results show that the main frequency bands that are different between MCI patients and controls are the θ and low α bands, and the differently affected brain areas are the frontal, left temporal and parietal areas. Compared to MCI patients, in patients with mild AD, the main frequency bands that are different are the low and high α bands, and the main differently affected brain region is a larger right temporal area. Four LOFC bands were used as input to train the ResNet-18 model. For the MCI dataset, the average accuracy of 20 runs was 93.42% and the best accuracy was 98.33%, while for the mild AD dataset, the average accuracy was 98.54% and the best accuracy was 100%. To determine the timing of early treatment and discovering the susceptible patients, and to slow the progression of the disease, we assume that the occurrence of MCI and mild AD and their progression to more serious AD and dementia could be inferred by analyzing the topological structure of the brain network generated by EEG. Our findings provide a novel solution for connectome-based biomarker analysis to improve personalized medicine.
Feng Duan; Zihao Huang; Zhe Sun; Yu Zhang; Qibin Zhao; Andrzej Cichocki; Zhenglu Yang; Jordi Sole-Casals. Topological Network Analysis of Early Alzheimer’s Disease Based on Resting-State EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2020, 28, 2164 -2172.
AMA StyleFeng Duan, Zihao Huang, Zhe Sun, Yu Zhang, Qibin Zhao, Andrzej Cichocki, Zhenglu Yang, Jordi Sole-Casals. Topological Network Analysis of Early Alzheimer’s Disease Based on Resting-State EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2020; 28 (10):2164-2172.
Chicago/Turabian StyleFeng Duan; Zihao Huang; Zhe Sun; Yu Zhang; Qibin Zhao; Andrzej Cichocki; Zhenglu Yang; Jordi Sole-Casals. 2020. "Topological Network Analysis of Early Alzheimer’s Disease Based on Resting-State EEG." IEEE Transactions on Neural Systems and Rehabilitation Engineering 28, no. 10: 2164-2172.
Gifted children learn more rapidly and effectively than others, presumably due to neurophysiological differences that affect efficiency in neuronal communication. Identifying the topological features that support its capabilities is relevant to understanding how the brain structure is related to intelligence. We proposed the analysis of the structural covariance network to assess which organizational patterns are characteristic of gifted children. The graph theory was used to analyse topological properties of structural covariance across a group of gifted children. The analysis was focused on measures of brain network integration, such as, participation coefficient and versatility, which quantifies the strength of specific modular affiliation of each regional node. We found that the gifted group network was more integrated (and less segregated) than the control group network. Brain regional nodes in the gifted group network had higher versatility and participation coefficient, indicating greater inter-modular communication mediated by connector hubs with links to many modules. Connector hubs of the networks of both groups were located mainly in association with neocortical areas (which had thicker cortex), with fewer hubs in primary or secondary neocortical areas (which had thinner cortex), as well as a few connector hubs in limbic cortex and insula. In the group of gifted children, a larger proportion of connector hubs were located in association cortex. In conclusion, gifted children have a more integrated and versatile brain network topology. This is compatible with the global workspace theory and other data linking integrative network topology to cognitive performance.
Jordi Solé-Casals; Josep M. Serra-Grabulosa; Rafael Romero-Garcia; Gemma Vilaseca; Ana Adan; Núria Vilaró; Núria Bargalló; Edward T. Bullmore. Structural brain network of gifted children has a more integrated and versatile topology. Brain Structure and Function 2019, 224, 2373 -2383.
AMA StyleJordi Solé-Casals, Josep M. Serra-Grabulosa, Rafael Romero-Garcia, Gemma Vilaseca, Ana Adan, Núria Vilaró, Núria Bargalló, Edward T. Bullmore. Structural brain network of gifted children has a more integrated and versatile topology. Brain Structure and Function. 2019; 224 (7):2373-2383.
Chicago/Turabian StyleJordi Solé-Casals; Josep M. Serra-Grabulosa; Rafael Romero-Garcia; Gemma Vilaseca; Ana Adan; Núria Vilaró; Núria Bargalló; Edward T. Bullmore. 2019. "Structural brain network of gifted children has a more integrated and versatile topology." Brain Structure and Function 224, no. 7: 2373-2383.
Face recognition or verification remains a real challenge in the area of pattern recognition and image processing. The image acquisition process is a crucial step in which noise will inevitably be introduced, and in most cases this noise drastically decreases the accuracy of the classification rate of recognition systems, making them ineffective. This paper presents a novel approach to face recognition or verification, which increases the recognition rate in noisy environmental conditions. The latter is achieved by using the intrinsic face mode functions that result from applying a bi-dimensional empirical mode decomposition with Green’s functions in tension to noisy images. Each image is individually decomposed, and noisy modes are discarded or filtered during reconstruction. Then, the extracted modes are used for classification purposes with canonical classifiers such as vector support machines or k-nearest neighbor classifiers. Experimental results show that this method achieves very stable results, almost independently of the amount of noise added to the image, due to the ability of decomposition to capture the noise in the first mode. Classification results using noisy images are at the same level as other algorithms proposed for the same databases but working on clean images and therefore are better than those obtained using classic image filters in noisy images. Moreover, unlike most of the available algorithms, the algorithm proposed in this paper is based on the input data (without the need to adjust parameters), making it transparent to the user. Finally, the proposed new approach achieves good results independently of the type of noise, the level of noise and the type of the database, which is not possible with other classical methods requiring parameter adjustment.
Saad Al-Baddai; Pere Marti-Puig; Esteve Gallego-Jutglà; Karema Al-Subari; Ana Maria Tomé; Bernd Ludwig; Elmar Wolfgang Lang; Jordi Solé-Casals. A recognition–verification system for noisy faces based on an empirical mode decomposition with Green’s functions. Soft Computing 2019, 24, 3809 -3827.
AMA StyleSaad Al-Baddai, Pere Marti-Puig, Esteve Gallego-Jutglà, Karema Al-Subari, Ana Maria Tomé, Bernd Ludwig, Elmar Wolfgang Lang, Jordi Solé-Casals. A recognition–verification system for noisy faces based on an empirical mode decomposition with Green’s functions. Soft Computing. 2019; 24 (5):3809-3827.
Chicago/Turabian StyleSaad Al-Baddai; Pere Marti-Puig; Esteve Gallego-Jutglà; Karema Al-Subari; Ana Maria Tomé; Bernd Ludwig; Elmar Wolfgang Lang; Jordi Solé-Casals. 2019. "A recognition–verification system for noisy faces based on an empirical mode decomposition with Green’s functions." Soft Computing 24, no. 5: 3809-3827.
Detecting and determining which systems or subsystems of a wind turbine have more failures is essential to improve their design, which will reduce the costs of generating wind power. Two of the most critical failures, the generator and gearbox, are analyzed and characterized with four metrics. This failure analysis usually begins with the identification of the turbine’s condition, a process normally performed by an expert examining the wind turbine’s service history. This is a time-consuming task, as a human expert has to examine each service entry. To automate this process, a new methodology is presented here, which is based on a set of steps to preprocess and decompose the service history to find relevant words and sentences that discriminate an unhealthy wind turbine period from a healthy one. This is achieved by means of two classifiers fed with the matrix of terms from the decomposed document of the training wind turbines. The classifiers can extract essential words and determine the conditions of new turbines of unknown status using the text from the service history, emulating what a human expert manually does when labelling the training set. Experimental results are promising, with accuracy and F-score above 90% in some cases. Condition monitoring system can be improved and automated using this system, which helps the expert in the tedious task of identifying the relevant words from the turbine service history. In addition, the system can be retrained when new knowledge becomes available and may therefore always be as accurate as a human expert. With this new tool, the expert can focus on identifying which systems or subsystems can be redesigned to increase the efficiency of wind turbines.
Alejandro Blanco-M.; Pere Marti-Puig; Karina Gibert; Jordi Cusidó; Jordi Solé-Casals. A Text-Mining Approach to Assess the Failure Condition of Wind Turbines Using Maintenance Service History. Energies 2019, 12, 1982 .
AMA StyleAlejandro Blanco-M., Pere Marti-Puig, Karina Gibert, Jordi Cusidó, Jordi Solé-Casals. A Text-Mining Approach to Assess the Failure Condition of Wind Turbines Using Maintenance Service History. Energies. 2019; 12 (10):1982.
Chicago/Turabian StyleAlejandro Blanco-M.; Pere Marti-Puig; Karina Gibert; Jordi Cusidó; Jordi Solé-Casals. 2019. "A Text-Mining Approach to Assess the Failure Condition of Wind Turbines Using Maintenance Service History." Energies 12, no. 10: 1982.
Brain-computer interface provides a new communication bridge between the human mind and devices, depending largely on the accurate classification and identification of non-invasive EEG signals. Recently, the deep learning approaches have been widely used in many fields to extract features and classify various types of data successfully. However, the deep learning approach requires massive data to train its neural networks, and the amount of data impacts greatly on the quality of the classifiers. This paper proposes a novel approach that combines deep learning and data augmentation for EEG classification. We applied the empirical mode decomposition on the EEG frames and mixed their intrinsic mode functions to create new artificial EEG frames, followed by transforming all EEG data into tensors as inputs of the neural network by complex Morlet wavelets. We proposed two neural networks-convolutional neural network and wavelet neural network-to train the weights and classify two classes of motor imagery signals. The wavelet neural network is a new type of neural network using wavelets to replace the convolutional layers. The experimental results show that the artificial EEG frames substantially improve the training of neural networks, and both two networks yield relatively higher classification accuracies compared to prevailing approaches. Meanwhile, we also verified the performance of our new proposed wavelet neural network model in the classification of steady-state visual evoked potentials.
Zhiwen Zhang; Feng Duan; Jordi Solé-Casals; Josep Dinares-Ferran; Andrzej Cichocki; Zhenglu Yang; Zhe Sun. A Novel Deep Learning Approach With Data Augmentation to Classify Motor Imagery Signals. IEEE Access 2019, 7, 15945 -15954.
AMA StyleZhiwen Zhang, Feng Duan, Jordi Solé-Casals, Josep Dinares-Ferran, Andrzej Cichocki, Zhenglu Yang, Zhe Sun. A Novel Deep Learning Approach With Data Augmentation to Classify Motor Imagery Signals. IEEE Access. 2019; 7 ():15945-15954.
Chicago/Turabian StyleZhiwen Zhang; Feng Duan; Jordi Solé-Casals; Josep Dinares-Ferran; Andrzej Cichocki; Zhenglu Yang; Zhe Sun. 2019. "A Novel Deep Learning Approach With Data Augmentation to Classify Motor Imagery Signals." IEEE Access 7, no. : 15945-15954.
It is well known that each year the wind sector has profit losses due to wind turbine failures and operation and maintenance costs. Therefore, operations related to these actions are crucial for wind farm operators and linked companies. One of the key points for failure prediction on wind turbine using SCADA data is to select the optimal or near optimal set of inputs that can feed the failure prediction (prognosis) algorithm. Due to a high number of possible predictors (from tens to hundreds), the optimal set of inputs obtained by exhaustive-search algorithms is not viable in the majority of cases. In order to tackle this issue, show the viability of prognosis and select the best set of variables from more than 200 analogous variables recorded at intervals of 5 or 10 min by the wind farm’s SCADA, in this paper a thorough study of automatic input selection algorithms for wind turbine failure prediction is presented and an exhaustive-search-based quasi-optimal (QO) algorithm, which has been used as a reference, is proposed. In order to evaluate the performance, a k-NN classification algorithm is used. Results showed that the best automatic feature selection method in our case-study is the conditional mutual information (CMI), while the worst one is the mutual information feature selection (MIFS). Furthermore, the effect of the number of neighbours (k) is tested. Experiments demonstrate that k = 1 is the best option if the number of features is higher than 3. The experiments carried out in this work have been extracted from measures taken along an entire year and corresponding to gearbox and transmission systems of Fuhrländer wind turbines.
Pere Marti-Puig; Alejandro Blanco-M; Juan José Cárdenas; Jordi Cusidó; Jordi Solé-Casals. Feature Selection Algorithms for Wind Turbine Failure Prediction. Energies 2019, 12, 453 .
AMA StylePere Marti-Puig, Alejandro Blanco-M, Juan José Cárdenas, Jordi Cusidó, Jordi Solé-Casals. Feature Selection Algorithms for Wind Turbine Failure Prediction. Energies. 2019; 12 (3):453.
Chicago/Turabian StylePere Marti-Puig; Alejandro Blanco-M; Juan José Cárdenas; Jordi Cusidó; Jordi Solé-Casals. 2019. "Feature Selection Algorithms for Wind Turbine Failure Prediction." Energies 12, no. 3: 453.
Essential tremor (ET) is the most common movement disorder. In fact, its prevalence is about 20 times higher than that of Parkinson's disease. In addition, studies have shown that a high percentage of cases, between 50 and 70%, are estimated to be of genetic origin. The gold standard test for diagnosis, monitoring and to differentiate between both pathologies is based on the drawing of the Archimedes' spiral. Our major challenge is to develop the simplest system able to correctly classify Archimedes' spirals, therefore we will exclusively use the information of the x and y coordinates. This is the minimum information provided by any digitizing device. We explore the use of features from drawings related to the Discrete Cosine Transform as part of a wider cross-study for the diagnosis of essential tremor held at Biodonostia. We compare the performance of these features against other classic and already analyzed ones. We outperform previous results using a very simple system and a reduced set of features. Because the system is simple, it will be possible to implement it in a portable device (microcontroller), which will receive the x and y coordinates and will issue the classification result. This can be done in real time, and therefore without needing any extra job from the medical team. In future works these new drawing-biomarkers will be integrated with the ones obtained in the previous Biodonostia study. Undoubtedly, the use of this technology and user-friendly tools based on indirect measures could provide remarkable social and economic benefits.
Jordi Solé-Casals; Iker Anchustegui-Echearte; Pere Marti-Puig; Pilar M. Calvo; Alberto Bergareche; José Ignacio Sánchez-Méndez; Karmele López-De-Ipiña. Discrete Cosine Transform for the Analysis of Essential Tremor. Frontiers in Physiology 2019, 9, 1947 .
AMA StyleJordi Solé-Casals, Iker Anchustegui-Echearte, Pere Marti-Puig, Pilar M. Calvo, Alberto Bergareche, José Ignacio Sánchez-Méndez, Karmele López-De-Ipiña. Discrete Cosine Transform for the Analysis of Essential Tremor. Frontiers in Physiology. 2019; 9 ():1947.
Chicago/Turabian StyleJordi Solé-Casals; Iker Anchustegui-Echearte; Pere Marti-Puig; Pilar M. Calvo; Alberto Bergareche; José Ignacio Sánchez-Méndez; Karmele López-De-Ipiña. 2019. "Discrete Cosine Transform for the Analysis of Essential Tremor." Frontiers in Physiology 9, no. : 1947.
The wind sectors pends roughly 2200M€ in repair the wind turbines failures. These failures do not contribute to the goal of reducing greenhouse gases emissions. The 25–35% of the generation costs are operation and maintenance services. To reduce this amount, the wind turbine industry is backing on the Machine Learning techniques over SCADA data. This data can contain errors produced by missing entries, uncalibrated sensors or human errors. Each kind of error must be handled carefully because extreme values are not always produced by data reading errors or noise. This document evaluates the impact of removing extreme values (outliers) applying several widely used techniques like Quantile, Hampel and ESD with the recommended cut-off values. Experimental results on real data show that removing outliers systematically is not a good practice. The use of manually defined ranges (static and dynamic) could be a better filtering strategy.
Pere Marti-Puig; Alejandro Blanco-M; Juan José Cárdenas; Jordi Cusidó; Jordi Solé-Casals. Effects of the pre-processing algorithms in fault diagnosis of wind turbines. Environmental Modelling & Software 2018, 110, 119 -128.
AMA StylePere Marti-Puig, Alejandro Blanco-M, Juan José Cárdenas, Jordi Cusidó, Jordi Solé-Casals. Effects of the pre-processing algorithms in fault diagnosis of wind turbines. Environmental Modelling & Software. 2018; 110 ():119-128.
Chicago/Turabian StylePere Marti-Puig; Alejandro Blanco-M; Juan José Cárdenas; Jordi Cusidó; Jordi Solé-Casals. 2018. "Effects of the pre-processing algorithms in fault diagnosis of wind turbines." Environmental Modelling & Software 110, no. : 119-128.
Dementia, and specially Alzheimer’s disease (AD) and Mild Cognitive Impairment (MCI) are one of the most important diseases suffered by elderly population. Music therapy is one of the most widely used non-pharmacological treatment in the field of cognitive impairments, given that music influences their mood, behavior, the decrease of anxiety, as well as facilitating reminiscence, emotional expressions and movement. In this work we present HAIDA, a multi-platform support system for Musical Therapy oriented to cognitive impairment, which includes not only therapy tools but also non-invasive biometric analysis, speech, activity and hand activity. At this moment the system is on use and recording the first sets of data. Results obtained using HAIDA will be presented in a near future after the analysis.
E. Fernandez; J. Solé-Casals; P. M. Calvo; M. Faundez-Zanuy; K. Lopez-De-Ipina. HAIDA: Biometric Technological Therapy Tools for Neurorehabilitation of Cognitive Impairment. Converging Clinical and Engineering Research on Neurorehabilitation 2018, 744 -748.
AMA StyleE. Fernandez, J. Solé-Casals, P. M. Calvo, M. Faundez-Zanuy, K. Lopez-De-Ipina. HAIDA: Biometric Technological Therapy Tools for Neurorehabilitation of Cognitive Impairment. Converging Clinical and Engineering Research on Neurorehabilitation. 2018; ():744-748.
Chicago/Turabian StyleE. Fernandez; J. Solé-Casals; P. M. Calvo; M. Faundez-Zanuy; K. Lopez-De-Ipina. 2018. "HAIDA: Biometric Technological Therapy Tools for Neurorehabilitation of Cognitive Impairment." Converging Clinical and Engineering Research on Neurorehabilitation , no. : 744-748.
In this work, we analyze the suppression of the power in mu and beta bands used in Motor-Imagery Brain Computer Interface systems (MI BCI) when using artificial frames. We compared the suppression effect between real and artificial frames. Experimental results in a single subject example show that artificial frames capture the same effect observed in real frames at a similar level. This interesting result supports the use of artificial frames during the BCI training process, which should reduce the number of real frames and hence reduce the calibration time in practical applications.
J. Dinarès-Ferran; M. Sebastián-Romagosa; R. Ortner; C. Guger; J. Solé-Casals. Exploring Bands Suppression in Artificial Frames for Motor-Imagery Brain Computer Interfaces. Converging Clinical and Engineering Research on Neurorehabilitation 2018, 739 -743.
AMA StyleJ. Dinarès-Ferran, M. Sebastián-Romagosa, R. Ortner, C. Guger, J. Solé-Casals. Exploring Bands Suppression in Artificial Frames for Motor-Imagery Brain Computer Interfaces. Converging Clinical and Engineering Research on Neurorehabilitation. 2018; ():739-743.
Chicago/Turabian StyleJ. Dinarès-Ferran; M. Sebastián-Romagosa; R. Ortner; C. Guger; J. Solé-Casals. 2018. "Exploring Bands Suppression in Artificial Frames for Motor-Imagery Brain Computer Interfaces." Converging Clinical and Engineering Research on Neurorehabilitation , no. : 739-743.
Among neural disorders related to movement, essential tremor has the highest prevalence; in fact, it is twenty times more common than Parkinson’s disease. The drawing of the Archimedes’ spiral is the gold standard test to distinguish between both pathologies. The aim of this paper is to select non-linear biomarkers based on the analysis of digital drawings. It belongs to a larger cross study for early diagnosis of essential tremor that also includes genetic information. The proposed automatic analysis system consists in a hybrid solution: Machine Learning paradigms and automatic selection of features based on statistical tests using medical criteria. Moreover, the selected biomarkers comprise not only commonly used linear features (static and dynamic), but also other non-linear ones: Shannon entropy and Fractal Dimension. The results are hopeful, and the developed tool can easily be adapted to users; and taking into account social and economic points of view, it could be very helpful in real complex environments.
Karmele Lopez-De-Ipina; Jordi Solé-Casals; Marcos Faúndez-Zanuy; Pilar M. Calvo; Enric Sesa; Josep Roure; Unai Martinez-De-Lizarduy; Blanca Beitia; Elsa Fernández; Jon Iradi; Joseba Garcia-Melero; Alberto Bergareche. Automatic Analysis of Archimedes’ Spiral for Characterization of Genetic Essential Tremor Based on Shannon’s Entropy and Fractal Dimension. Entropy 2018, 20, 531 .
AMA StyleKarmele Lopez-De-Ipina, Jordi Solé-Casals, Marcos Faúndez-Zanuy, Pilar M. Calvo, Enric Sesa, Josep Roure, Unai Martinez-De-Lizarduy, Blanca Beitia, Elsa Fernández, Jon Iradi, Joseba Garcia-Melero, Alberto Bergareche. Automatic Analysis of Archimedes’ Spiral for Characterization of Genetic Essential Tremor Based on Shannon’s Entropy and Fractal Dimension. Entropy. 2018; 20 (7):531.
Chicago/Turabian StyleKarmele Lopez-De-Ipina; Jordi Solé-Casals; Marcos Faúndez-Zanuy; Pilar M. Calvo; Enric Sesa; Josep Roure; Unai Martinez-De-Lizarduy; Blanca Beitia; Elsa Fernández; Jon Iradi; Joseba Garcia-Melero; Alberto Bergareche. 2018. "Automatic Analysis of Archimedes’ Spiral for Characterization of Genetic Essential Tremor Based on Shannon’s Entropy and Fractal Dimension." Entropy 20, no. 7: 531.
One of the current issues in brain-computer interface (BCI) is how to deal with noisy electroencephalography (EEG) measurements organized as multidimensional datasets (tensors). On the other hand, recently, significant advances have been made in multidimensional signal completion algorithms that exploit tensor decomposition models to capture the intricate relationship among entries in a multidimensional signal. We propose to use tensor completion applied to EEG data for improving the classification performance in a motor imagery BCI system with corrupted measurements. Noisy measurements (electrode misconnections, subject movements, etc.) are considered as unknowns (missing samples) that are inferred from a tensor decomposition model (tensor completion). We evaluate the performance of four recently proposed tensor completion algorithms, CP-WOPT (Acar et al. Chemom Intell Lab Syst. 106:41-56, 2011), 3DPB-TC (Caiafa et al. 2013), BCPF (Zhao et al. IEEE Trans Pattern Anal Mach Intell. 37(9):1751-1763, 2015), and HaLRT (Liu et al. IEEE Trans Pattern Anal Mach Intell. 35(1):208-220, 2013), plus a simple interpolation strategy, first with random missing entries and then with missing samples constrained to have a specific structure (random missing channels), which is a more realistic assumption in BCI applications. We measured the ability of these algorithms to reconstruct the tensor from observed data. Then, we tested the classification accuracy of imagined movement in a BCI experiment with missing samples. We show that for random missing entries, all tensor completion algorithms can recover missing samples increasing the classification performance compared to a simple interpolation approach. For the random missing channels case, we show that tensor completion algorithms help to reconstruct missing channels, significantly improving the accuracy in the classification of motor imagery (MI), however, not at the same level as clean data. Summarizing, compared to the interpolation case, all tensor completion algorithms succeed to increase the classification performance by 7–9% (LDA–SVD) for random missing entries and 15–8% (LDA–SVD) for random missing channels. Tensor completion algorithms are useful in real BCI applications. The proposed strategy could allow using motor imagery BCI systems even when EEG data is highly affected by missing channels and/or samples, avoiding the need of new acquisitions in the calibration stage.
J. Solé-Casals; C. F. Caiafa; Q. Zhao; A. Cichocki. Brain-Computer Interface with Corrupted EEG Data: a Tensor Completion Approach. Cognitive Computation 2018, 10, 1062 -1074.
AMA StyleJ. Solé-Casals, C. F. Caiafa, Q. Zhao, A. Cichocki. Brain-Computer Interface with Corrupted EEG Data: a Tensor Completion Approach. Cognitive Computation. 2018; 10 (6):1062-1074.
Chicago/Turabian StyleJ. Solé-Casals; C. F. Caiafa; Q. Zhao; A. Cichocki. 2018. "Brain-Computer Interface with Corrupted EEG Data: a Tensor Completion Approach." Cognitive Computation 10, no. 6: 1062-1074.