SincTran: An end-to-end transformer pipeline for imagined speech electroencephalogram classification

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Abstract
Imagined speech decoding from electroencephalography (EEG) is a challenging problem in brain-computer interfaces (BCI), due to the poor signal-to-noise ratio in non-invasive systems and the complex nature of language decoding. This study proposes an end-to-end deep learning model, SincTran, which internalizes the decomposition of EEG signals into their physiological frequency bands. The proposed model utilizes parallel sinc convolution layers, each tuned to a specific EEG band, aggregated via a softplus-scored, sum-normalized band attention mechanism, followed by spatio-temporal convolution layers and a transformer encoder. The encoded class tokens are passed to a multi-layer perceptron classification head to produce the final predictions. SincTran was evaluated on two benchmark datasets of imagined speech, achieving competitive classification performance across all tasks while maintaining favorable parameter and computational efficiency relative to comparable classifiers. On the 2020 BCI Competition dataset, the model reached an average accuracy of 65.75%, and on the Arizona State University dataset, it reached 79.35% accuracy on the long-word vs. short-word classification task. Additionally, SincTran supports direct interpretability through its architecture, enabling analysis of EEG band importance, temporal attention distribution, and class token geometry. Gradient-weighted class activation mapping was further employed for post-hoc analysis of channel importance. The findings demonstrate the viability of end-to-end pipelines that internalize spectral decomposition for imagined speech EEG classification.

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