Emotions composed of cognizant logical reactions toward various situations. Such mental responses stem from physiological, cognitive, and behavioral changes. Electroencephalogram (EEG) signals provide a noninvasive and nonradioactive solution for emotion identification. Accurate and automatic classification of emotions can boost the development of human-computer interface. This article proposes automatic extraction and classification of features through the use of different convolutional neural networks (CNNs). At first, the proposed method converts the filtered EEG signals into an image using a time-frequency representation. Smoothed pseudo-Wigner-Ville distribution is used to transform time-domain EEG signals into images. These images are fed to pretrained AlexNet, ResNet50, and VGG16 along with configurable CNN. The performance of four CNNs is evaluated by measuring the accuracy, precision, Mathew's correlation coefficient, F1-score, and false-positive rate. The results obtained by evaluating four CNNs show that configurable CNN requires very less learning parameters with better accuracy. Accuracy scores of 90.98%, 91.91%, 92.71%, and 93.01% obtained by AlexNet, ResNet50, VGG16, and configurable CNN show that the proposed method is best among other existing methods.

Diagnosis of schizophrenia (SZ) is traditionally performed through patient’s interviews by a skilled psychiatrist. This process is time-consuming, burdensome, subject to error and bias. Hence the aim of this study is to develop an automatic SZ identification scheme using electroencephalogram (EEG) signals that can eradicate the aforementioned problems and support clinicians and researchers. This study introduces a methodology design involving empirical mode decomposition (EMD) technique for diagnosis of SZ from EEG signals to perfectly handle the behavior of non-stationary and nonlinear EEG signals. In this study, each EEG signal is decomposed into intrinsic mode functions (IMFs) by the EMD algorithm and then twenty-two statistical characteristics/features are calculated from these IMFs. Among them, five features are selected as significant feature applying Kruskal Wallis test. The performance of the obtained feature set is tested through several renowned classifiers on a SZ EEG dataset. Among the considered classifiers, the ensemble bagged tree performed as the best classifier producing 93.21% correct classification rate for SZ, with an overall accuracy of 89.59% for IMF 2. These results indicate that EEG signals discriminate SZ patients from healthy control (HC) subjects efficiently and have the potential to become a tool for the psychiatrist to support the positive diagnosis of SZ.

Schizophrenia (SZ) is a psychiatric disorder characterized by cognitive dysfunctions, hallucinations, and delusions which may lead to lifetime disability. Detection and diagnosis of SZ by visual inspection is subjective, difficult and time-consuming. Electroencephalogram (EEG) signals are widely used to detect the SZ as they reflect the conditions of the brain. Conventional machine learning methods involve many lengthy manual steps like decomposition, feature extraction, feature selection, and classification. In this paper, automated identification of SZ is proposed using combination of time-frequency analysis and convolutional neural network (CNN) to overcome the limitations of feature extraction-based methods. Three press button tasks are analyzed to segregate normal subjects and SZ patients. The EEG signals are subjected to continuous wavelet transform, short-time Fourier transform, and smoothed pseudo-Wigner Ville distribution (SPWVD) techniques to obtain scalogram, spectrogram, and SPWVD-based time frequency representation (TFR) plots respectively. These two-dimensional (2D) plots are fed to pretrained AlexNet, VGG16, ResNet50 and CNN. We have obtained an accuracy of 93.36% using SPWVD-based TFR and CNN model. In comparison to the benchmark AlexNet, ResNet50, and VGG16 networks, the developed CNN model with four convolutional layers not only require fewer learnable parameters, but also computationally efficient and fast. This clearly indicates that our proposed method combing SPWVD-CNN model has performed better than the state-of-the-art transfer learning techniques. Our developed model is ready to be tested with more EEG data and can aid psychiatrists in their diagnosis.

Parkinson’s disease (PD) is a neurodegenerative ailment which causes changes in the neuronal, behavioral, and physiological structures. During the early stages of PD, these changes are very subtle and hence accurate diagnosis is challenging. Pathological and neurological experts assess the PD patients by examining their drawing, writing, walking, tremor, facial expressions, and speech. The manual analysis performed by specialists is time-consuming and prone to errors. Electroencephalogram (EEG) signals represent changes in the brain activities, but it is difficult to manually analyze these signals due to their non-linear, non-stationary, and complex nature. Traditional machine learning methods require several manual steps, such as decomposition, extraction of features, and classification. To overcome these limitations, automated PD detection using smoothed pseudo-Wigner Ville distribution (SPWVD) coupled with convolutional neural networks (CNN) called Parkinson’s disease CNN (PDCNNet) is proposed. First the EEG signals are subjected to SPWVD to obtain time-frequency representation (TFR). Then these two-dimensional plots are fed to an CNN. The proposed model is developed using two public databases. We have obtained an accuracy of 100% and 99.97% for dataset 1 and 2, respectively in detecting PD automatically using our proposed PDCNNet model. Our developed prototype has outperformed all existing state-of-the-art techniques and ready to be validated with more diverse datasets.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects a person's sleep, mood, anxiety, and learning. Early diagnosis and timely medication can help individuals with ADHD perform daily tasks without difficulty. Electroencephalogram (EEG) signals can help neurologists to detect ADHD by examining the changes occurring in it. The EEG signals are complex, non-linear, and non-stationary. It is difficult to find the subtle differences between ADHD and healthy control EEG signals visually. Also, making decisions from existing machine learning (ML) models do not guarantee similar performance (unreliable). The paper explores a combination of variational mode decomposition (VMD), and Hilbert transform (HT) called VMD-HT to extract hidden information from EEG signals. Forty-one statistical parameters extracted from the absolute value of analytical mode functions (AMF) have been classified using the explainable boosted machine (EBM) model. The interpretability of the model is tested using statistical analysis and performance measurement. The importance of the features, channels and brain regions has been identified using the glass-box and black-box approach. The model's local and global explainability has been visualized using Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), Partial Dependence Plot (PDP), and Morris sensitivity. To the best of our knowledge, this is the first work that explores the explainability of the model prediction in ADHD detection, particularly for children. Our results show that the explainable model has provided an accuracy of 99.81%, a sensitivity of 99.78%, 99.84% specificity, an F-1 measure of 99.83%, the precision of 99.87%, a false detection rate of 0.13%, and Mathew's correlation coefficient, negative predicted value, and critical success index of 99.61%, 99.73%, and 99.66%, respectively in detecting the ADHD automatically with ten-fold cross-validation. The model has provided an area under the curve of 100% while the detection rate of 99.87% and 99.73% has been obtained for ADHD and HC, respectively. The model show that the interpretability and explainability of frontal region is highest compared to pre-frontal, central, parietal, occipital, and temporal regions. Our findings has provided important insight into the developed model which is highly reliable, robust, interpretable, and explainable for the clinicians to detect ADHD in children. Early and rapid ADHD diagnosis using robust explainable technologies may reduce the cost of treatment and lessen the number of patients undergoing lengthy diagnosis procedures.

Background: Emotion is a neuronic transient that drives a person to certain action. Emotion recognition from electroencephalogram (EEG) signals plays a vital role in the development of a brain-computer interface (BCI). Extracting the important information from raw EEG signals is difficult due to its non-stationary nature. Fixing a factual predefined basis function for efficient decomposition using tunable Q wavelet transform is an arduous task. Method: In this paper, an adaptive tunable Q wavelet transform is proposed for the automatic selection of tuning parameters. Optimum tuning parameters are obtained using grey wolf optimization (GWO). Tuning parameters obtained by GWO are used to decompose the EEG signals into subbands. Set of time-domain features elicited from the subbands are used as an input to multiclass least square support vector machine. Results: Classification accuracy of four basic emotions viz. happy, fear, sad, and relax is tested and compared with existing methods. An accuracy of 95.70% is achieved with radial basis function kernel which is about 5% more than the existing methods using the same dataset. Conclusion: This paper proposes the development of a non-parameterized decomposition method for efficient decomposition of EEG signals. This method can be used with machine learning algorithms to take a step forward in the development of BCI systems.