Deep Learning Techniques for Brain–Computer Interface Enabled Assistive Communication Systems

Abstract

Brain–computer interface (BCI) enabled assistive communication systems seek to restore or augment communication for individuals with severe motor impairment by translating neural activity into control commands for spelling, text generation, environmental control, or speech prostheses. Recent progress has been driven by deep learning, which has altered the methodological landscape from handcrafted feature engineering toward end-to-end representation learning across electroencephalography (EEG), electrocorticography (ECoG), and related biosignals. Between 2021 and 2026, the field has expanded from conventional convolutional neural network pipelines for motor imagery and event-related potentials toward transformer-based sequence modeling, multimodal fusion, transfer learning, adaptive calibration, and benchmark-oriented software ecosystems. These advances are particularly important for assistive communication because communication-oriented BCIs must achieve high information transfer under constraints of fatigue, limited training data, non-stationary physiology, and real-world usability. This review synthesizes the technical foundations, architectural patterns, and implementation choices that shape deep learning driven assistive communication BCIs. It classifies systems by recording modality, neural paradigm, decoding objective, adaptation strategy, and communication output layer, then examines model families including convolutional, recurrent, graph-based, generative, self-supervised, and transformer architectures. The review further analyzes public datasets, benchmark frameworks, software libraries, and evaluation metrics that underpin reproducible research, with emphasis on BCI Competition IV and MOABB-supported datasets widely used for algorithm comparison. Critical attention is given to practical translation issues, including domain shift, calibration burden, interpretability, ethics, safety, and equitable deployment. By integrating recent literature with system-level analysis, the paper argues that the next generation of assistive communication BCIs will depend less on isolated decoder accuracy and more on closed-loop co-adaptation, multimodal language integration, user-specific personalization, and clinically grounded validation pathways