International Journal of Science, Engineering and TechnologyAuthors: Research Scholar Dhome Pranav Pradip, Professor Bangar Abhishek Popat, Professor Bhosale Sachin Bajirao
Abstract: Brain-computer interfaces establish a communication pathway between neural activity and external devices, offering opportunities for assistive interaction, motor rehabilitation, and cognitive-state monitoring. Despite advances in electroencephalography and artificial intelligence, practical deployment remains limited by noisy signals, variation across users, calibration burden, computational constraints, and uneven clinical evidence. This review examines five publications from 2023 to 2026 that address complementary aspects of these challenges: adaptive rehabilitation after stroke, tactile stimulation for cross-subject motor-imagery calibration, convolutional Transformer-based EEG decoding, embedded Edge AI systems, and clinical translation. A structured narrative synthesis is used to compare study objectives, methods, evaluation settings, findings, and limitations. The reviewed evidence suggests that adaptive feedback can support participation by users with poor initial BCI control, while tactile calibration may reduce dependence on demanding imagery tasks. Hierarchical temporal feature learning improves performance on selected cognitive classification benchmarks, but benchmark accuracy does not establish rehabilitation efficacy or reliable operation in everyday environments. Edge deployment introduces additional requirements for energy efficiency, latency, memory use, and data protection. Based on these findings, a conceptual seven-stage EEG architecture is proposed to connect signal-quality assessment, individualized calibration, efficient decoding, confidence-aware feedback, and monitored adaptation. The architecture is not experimentally validated. Future work should prioritize independent participant evaluation, prospective online studies, clinically meaningful outcomes, and transparent reporting of the trade-offs between accuracy, usability, and hardware efficiency.
