A Low-Cost Single-Channel EEG Brain–Computer Interface for Decoding Binary Commands from Self-Generated Emotional States

Brain–computer interface (BCI) systems aim to establish direct communication pathways between neural activity and external devices, enabling interaction without relying on conventional neuromuscular mechanisms. This study investigates the feasibility of decoding binary decisions (“Yes”/”No”) from self-generated cognitive–emotional modulation patterns using a single-channel low-cost EEG device. The proposed approach evaluates whether internally generated modulation strategies can produce distinguishable neural activity suitable for BCI applications under constrained acquisition conditions. EEG signals were recorded from two participants using a consumer-grade headset while they responded to questions through intentional internal modulation associated with affirmative and negative responses. The recorded signals were preprocessed, and multiple feature representations were extracted, including raw temporal data, cepstral coefficients, spectral power, and continuous wavelet transform (CWT) features. Several machine learning and deep learning models, including convolutional neural networks (CNN), long short-term memory networks (LSTM), and support vector machines (SVM), were trained and evaluated using hold-out and stratified k-fold validation strategies. The best performance was achieved by a CWT-based CNN model, reaching an average accuracy of 80.5%, significantly above chance level. Additional models, including CEP-CNN and RAW-LSTM, achieved competitive results, highlighting the relevance of feature representation in EEG-based classification tasks. The results demonstrate that internally generated modulation patterns can produce distinguishable EEG responses, even when using low-cost single-channel hardware. Although the limited number of participants constrains statistical generalization, this work serves as a proof-of-concept and provides a reproducible experimental pipeline for future studies. Overall, the findings support the development of accessible, scalable, and user-centered BCI systems based on internally generated neural modulation strategies, contributing to more natural interaction paradigms in EEG-based communication systems.