Introduction Identification and treatment of neurological disorders depend much on brain imaging and neurotherapeutic decision support. Although they are loud, do not remain in one spot, and are rather complex, electroencephalogram (EEG) signals are the principal tool used in research of brain function. This work employs an Adaptive Transformer-based technique with improved attention processes to extract temporal and spatial relationships in EEG data, effectively addressing these issues. Methods First processed to eliminate noise and split them into time-series chunks, EEG data are then included into the proposed approach. Channel-wise embeddings and temporal encoding help to depict the data. Then, a transformer design including spatial attention for inter-channel interactions, multi-head self-attention for temporal aspects, and an adaptive attention mask for domain-specific modifications is used. Other openly accessible EEG datasets as well as the TUH EEG Corpus and CHB-MIT were evaluated against the model. Its performance was scored using metrics like accuracy, precision, memory, and F1-score. Results The suggested method was more accurate than standard models like CNNs and LSTMs, with a score of 98.24%. The method was also shown to be able to find minor patterns in EEG data by improving precision and memory. Attention maps showed important areas of time and space, which made them easier to understand and useful in professional settings. Discussion The Adaptive Transformer turns out to be a useful tool for neurotherapeutic use of EEG data modeling. The approach provides greater medical assistance and knowledge on the functioning of the brain as well as answers significant issues. Future research might focus on subject-specific modifications and interaction with real-time systems. Conclusion This study demonstrates the potential of transformer-based models in revolutionizing EEG analysis for precision brain imaging and neurotherapeutic decision-making.
Nemade et al. (2025) studied this question.