Optimizing motor imagery BCI models with hard trials removal and model refinement.
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Abstract
Deep learning models have demonstrated remarkable performance in the classification of motor imagery BCI systems. However, these models exhibit sensitivity to challenging trials, often called hard trials, leading to performance degradation. In this paper, we address this issue by proposing two novel methods for identifying and mitigating the impact of hard trials on model performance. The first method leverages model prediction scores to discern hard trials. The second approach employs a quantitative explainable artificial intelligence (XAI) approach, enabling a more transparent and interpretable means of hard trials identification.
The identified hard trials are removed from the entire motor imagery training and validation dataset, and the deep learning model is further re-trained using the dataset without hard trials.
To evaluate the efficacy of these proposed methods, experiments were conducted on the Open
BMI dataset. The results for hold-out analysis show that, the proposed quantitative XAI based hard trial removal method has statistically improved the average classification accuracy of the baseline deep CNN model from 63.77 % to 68.70 %, with p-value = 7.66 -11 for the subject specific MI classification. Additionally, analyzing the scalp map depicting the average relevance scores of correctly classified trials compared to a misclassified trial provides a deeper insight into identifying hard trials. The results indicates that the proposed quantitative based XAI approach outperforms the prediction-score based approach in hard trial identification.
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