Background: Prediction of Gait intention from pre-movement Electroencephalography (EEG) signals is a vital step in developing a real-time Brain-computer Interface (BCI) for a proper neuro-rehabilitation system. In that respect, this paper investigates the feasibility of a fully predictive methodology to detect the intention to start and stop a gait cycle by utilizing EEG signals obtained before the event occurrence. Methods: An eight-channel, custom-made, EEG system with electrodes placed around the sensorimotor cortex was used to acquire EEG data from six healthy subjects and two amputees. A discrete wavelet transform-based method was employed to capture event related information in alpha and beta bands in the time-frequency domain. The Hjorth parameters, namely activity, mobility, and complexity, were extracted as features while a two-sample unpaired Wilcoxon test was used to get rid of redundant features for better classification accuracy. The feature set thus obtained was then used to classify between ’walk vs. stop’ and ’rest vs. start’ classes using support vector machine (SVM) classifier with RBF kernel in a ten-fold cross-validation scheme. Results: Using a fully predictive intention detection system, 76.41±4.47% accuracy, 72.85±7.48% sensitivity, and 79.93±5.50% specificity were achieved for ’rest vs. start’ classification. While for ’walk vs. stop’ classification, the obtained mean accuracy, sensitivity, and specificity were 74.12±4.12%, 70.24±6.45%, and 77.78±7.01% respectively. Overall average True Positive Rate achieved by this methodology was 72.06±8.27% with 1.45 False Positives/min. Conclusion: Extensive simulations and resulting classification results show that it is possible to achieve statistically similar intention detection accuracy using either only pre-movement EEG features or trans-movement EEG features. The classifier performance shows the potential of the proposed methodology to predict human movement intention exclusively from the pre-movement EEG signal to be applied in real-life prosthetic and neuro-rehabilitation systems.

Human Computer Interface and implicit emotion tagging of multimedia contents require determination of various emotional states. This paper presents a new emotion recognition method based on wavelet analysis of Empirical Mode Decomposed (EMD) Electroencephalogram (EEG) signals responsive to music videos. Discrete Wavelet Transform (DWT) is performed on the selected Intrinsic Mode Functions (IMFs) obtained from EMD operation. Then Higher Order Statistics (HOS), namely, variance, kurtosis, skewness of suitably chosen DWT coefficients are exploited to form feature vector. Furthermore, Principal Component Analysis (PCA) is applied on the feature vector to reduce the feature dimension. The reduced feature set thus obtained is then fed to Support Vector Machine (SVM) to perform two class classification of emotions. Extensive simulations are carried out to test the efficacy of the proposed method using DEAP, an affective computing database. It is found that the proposed method outperforms some state of the art methods of similar emotion recognition using the same database.

Prediction of human voluntary gait intention is a very significant task to ensure direct cortical control of real-life assistive technologies for locomotion rehabilitation. Neurophysiological studies provide that human voluntary gait intention is represented by slow DC potentials and power shifts in specific frequency ranges of brain wave, which can be detected 1.5- 2 seconds before the actual onset. The goal of this study was to determine whether it is possible to reliably detect the intention of voluntary gait 'starting' and 'stopping' intention before it takes place. A computational algorithm was designed to implement asynchronous prediction of gait intention in an offline and pseudo-online environment using support vector machine. Six healthy subjects participated in the study and performed self- paced voluntary gait cycles. A combination of advanced wavelet transform algorithms resulted in 88.23±1.59% accuracy, 85.42±4.03% sensitivity and 90.24±2.78% specificity for intention of start detection and 87.04±1.72% accuracy, 82.69±4.13% sensitivity and 89.59±3.04% specificity for intention to stop walking in offline testing. Additionally, the wavelet transform methods accompanied with threshold regulation and majority voting algorithm resulted in a True Positive Rate of 85.5±5.0% and 81.2±3.3% for ‘start’ and ‘stop’ prediction with 6.8±0.7 and 9.4±1.0 False Positives per Minute respectively in pseudo online testing. The average detection latencies were -1002±603 ms and -943±603 ms, respectively, for 'start' and 'stop' prediction. The study provides promising outcomes in terms of TPR, FP/min, and detection latency, which suggests that human voluntary gait intention can be predicted before the onset of movement.

Recent psychological and neurological studies suggest that human motor preparation and execution are largely affected by the subjective emotional state. Thus, external emotion stimuli can be a potential tool to enhance the detectability of movement intention from pre-movement neural signals. This study investigated whether emotion-evoking music stimulus could improve the performances of a fully predictive Brain-Computer Interface (BCI) system for movement intention detection. For this purpose, electroencephalographical (EEG) signals were recorded from twelve healthy subjects under three emotional conditions: happy, sad, and neutral. The emotions were elicited using external music stimuli while they performed a wrist extension action. Additionally, support vector machine-based offline and pseudo online testing schemes were employed to solve a binary classification problem for determining movement intention from pre-movement EEG. EEG power analysis showed that happy music stimulus resulted in an early occurrence of event-related desynchronization in alpha band compared other emotional states. Happy emotional stimuli also resulted in comparatively better performances in both offline and pseudo online testing paradigms. The results of this study suggest that external happy music stimulus could enhance the early and accurate detectability of human self-paced movement intention and thus could contribute to the predictive capability of state-of-the-art assistive BCIs.

In neurorehabilitation systems, early detection of gait intention with a lower false-positive rate and higher sensitivity is a critical factor which dictates the successful operation of the exoskeleton or assistive system. Traditional supervised learning algorithms often fail to reach these goals due to the lack of ability to adapt to the diverse interaction between the external environment and the human user. A threshold regulation approach might be a simple yet significant addition to increasing the adaptability of the intention detection system. In this paper, the performance of a pseudo-online BCI system in asynchronous detection of human gait intention from movement-related Electroencephalography signals is investigated. Seven healthy individuals participated in the study who performed self-paced cycles of gait initiation and termination in multiple trials. A custom-made eight-channel EEG system was used to capture the movement-related neural signals while a pair of in-sole pressure sensors and an Electromyography (EMG) sensor were used for time locking the actual moments of movement onset and termination. A wavelet transform based method along with Hjorth parameters, was employed to extract informative features which were then used to train an SVM-RBF classifier with threshold regulation for asynchronous detection of gait intention. A high true positive rate, low false-positive rate, and very low latency were achieved by the proposed methodology. The results demonstrate the feasibility of the proposed framework in building a human-in-the-loop neurorehabilitation system.

Volitional control of prosthetic devices can potentially provide the user with a more natural movement experience. Currently, there is no feasible volitional triggering method to adapt the prosthetic device to user's intention to accelerate during walking. Therefore, real-time prediction of human acceleration intention from the pre-acceleration electroencephalogram (EEG), and subsequent adaptation of the prosthetic device's control parameters for seamless transition remains a daunting research area. In that aspect, this study investigates the neural changes responsive to human intention to accelerate during walking. Furthermore, this study also explores whether the acceleration intention can be predicted from real-time EEG to subsequently enable parametric adaptation for an external prosthetic device. EEG, Inertial Measurement Unit (IMU), and ground reaction force (GRF) signals were collected from one healthy subject during walking with self-paced speed changes. A set of classifiers were explored to classify between constant speed and acceleration. The classifiers showed promising classification performance well above chance level in offline, pseudo-online and real-time scenarios. An accuracy of 85.9±2.9% was achieved in offline scenario, while pseudo-online classification resulted in a true positive rate (TPR) of 81.9±7.4% with 7.7±0.8 false positives/ min and a detection latency of -844±572 ms. In real-time scenario, 9 out of 12 acceleration events were predicted successfully with only 3 false predictions at an average latency of -741 ms. Moreover, offline data analysis suggested suppression of mu and beta rhythms related to gait acceleration between 2 seconds before and 1.5 seconds after the onset of acceleration. A slow increase in negative amplitude was also observed in near DC frequencies of EEG data acquired from the sensorimotor cortex. The findings of this study portray inspiring results in retrieving gait acceleration-associated neural changes for facilitating natural control of assistive devices.