Human–machine interface (HMI) systems are increasingly utilized to develop assistive technologies for individuals with disabilities and older adults. This study proposes two HMI systems using piezoelectric sensors to detect facial muscle activations from eye and tongue movements, and accelerometers to monitor head movements. This system enables hands-free wheelchair control for those with physical disabilities and speech impairments. A prototype wearable sensing device was also designed and implemented. Four commands can be generated using each sensor to steer the wheelchair. We conducted tests in offline and real-time scenarios to assess efficiency and usability among older volunteers. The head–machine interface achieved greater efficiency than the face–machine interface. The simulated wheelchair control tests showed that the head–machine interface typically required twice the time of joystick control, whereas the face–machine interface took approximately four times longer. Participants noted that the head-mounted wearable device was flexible and comfortable. Both modalities can be used for wheelchair control, especially the head–machine interface for patients retaining head movement. In severe cases, the face–machine interface can be used. Moreover, hybrid control can be employed to satisfy specific requirements. Compared to current commercial devices, the proposed HMIs provide lower costs, easier fabrication, and greater adaptability for real-world applications. We will further verify and improve the proposed devices for controlling a powered wheelchair, ensuring practical usability for people with paralysis and speech impairments.

Research on self-imagined emotional imagery supports the development of practical affective brain–computer interface (BCI) systems. This study proposes a hybrid emotion induction approach that combines facial expression image cues with subsequent emotional imagery, involving six positive and six negative emotions across two- or four-class valence and arousal categories. Machine learning (ML) techniques were applied to interpret these self-generated emotions from electroencephalogram (EEG) signals. Experiments were conducted to observe brain activity and validate the proposed feature and classification algorithms. The results showed that absolute beta power features computed from power spectral density (PSD) across EEG channels consistently achieved the highest classification accuracy for all emotion categories with the K-nearest neighbors (KNN) algorithm, while alpha–beta ratio features also contributed. The nonlinear parametric ML models achieved high effectiveness; the K-nearest neighbor (KNN) classifier performed best in detecting neutral states, while the artificial neural network (ANN) achieved balanced accuracy across emotional stages. The proposed system supports the use of the hybrid emotion induction paradigm and PSD-derived EEG features to develop reliable, subject-independent affective BCI systems. In future work, we will expand the datasets, employ advanced feature extraction and deep learning models, integrate multi-modal signals, and validate the proposed approaches across broader populations.

Self-imagined emotional imagery, in which individuals internally generate emotional states without external induction, plays a key role in shaping affective brain–computer interface (BCI) systems. Robust generalization across individuals is challenging due to significant variability in EEG patterns. This study evaluates a multi-domain electroencephalography (EEG) decoding framework for self-imagined emotion recognition, integrating time–frequency features, functional connectivity, effective connectivity, and subject-specific calibration. EEG data were collected from 38 participants and categorized into four emotional conditions using the valence–arousal model. Time–frequency features were extracted using the wavelet transform; functional connectivity was assessed using the phase locking value (PLV) and Pearson’s correlation; and effective connectivity was estimated using Granger causality. For comparison, a two-dimensional convolutional neural network (CNN) based on the continuous wavelet transform was implemented. Under pooled sample-wise cross-validation, PLV-based features combined with a support vector machine (SVM) achieved 97.0% accuracy, while the CNN reached 94.75%. Under leave-one-subject-out evaluation, accuracy declined to approximately 25–30%, indicating limited generalization to unseen participants. With subject-specific calibration using 75% of the target participant’s data, the SVM achieved 95.0% accuracy. The results support the recognition of four emotional conditions under the evaluated protocol when data from the target participant are available. However, cross-subject generalizability remains a significant limitation. As the study was conducted under controlled experimental conditions, further research involving independent populations and experimental settings is necessary to evaluate the framework’s robustness and applicability.

Although electroencephalography (EEG)-based emotion recognition is a promising approach for affective brain–computer interface (BCI) applications, substantial inter-subject variability continues to limit its generalizability. This study proposed an EEG-based framework to recognize emotions within a valence–arousal model using auditory stimulation. Predefined emotional states were established using validated affective video clips and subsequently evaluated through EEG responses elicited by instrumental melodies. Three EEG features, which include discrete wavelet transform (DWT), functional connectivity (FC), and effective connectivity (EC), together with their combined feature set, were systematically evaluated using five machine learning classifiers. Performance was assessed under subject-dependent, subject-independent (leave-one-subject-out, LOSO), and few-shot subject-adaptation protocols. The results showed that DWT achieved the highest subject-dependent classification accuracy (0.88), followed by the combined feature set (0.84). In contrast, the subject-independent LOSO evaluation yielded near-chance performance across all feature domains (0.23–0.30), highlighting substantial inter-subject variability. Few-shot subject adaptation using 25–75% subject-specific calibration data substantially improved subject-independent performance, with the highest accuracy of 0.77 achieved by FC at 75% calibration. In conclusion, these findings demonstrate the feasibility of EEG-based emotion recognition using auditory stimulation under predefined affective conditions and provide a foundation for the future development of personalized affective brain–computer interface systems.