Advancing Motor Imagery EEG Classification through Wavelet Scattering Transforms and 1D Transformers
Year: 2025
Type: conference-paper
Abstract: Motor imagery (MI) electroencephalography (EEG) classification is critical to brain-computer interfaces (BCI). Existing techniques failed mainly because of the noise and non-linearity features of EEG signals. This research work develops a new approach that exploits wavelet scattering transform (WST) for feature extraction along with the 1D transformer models for classification. WST captures time-frequency information and preserves locality of the signal; the 1D transformer uses its self-attention mechanism to take advantage of sequential data and capture long-range dependencies, strengthening the model’s capacity to focus only on relevant features in the signal. The proposed method achieves an accuracy of 98.10% on a well-known EEG dataset, surpassing the performance of several existing methods. The model was evaluated on other metrics like precision, recall, and F1 score, giving an average value of 98%. This work further advances the MI EEG classification, and more robust and accurate BCI systems can be laid by making use of the best of both WST and transformer architectures.
Source: Procedia Computer Science
Institution: National Institute of Technology Goa
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W4410258157