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Knowledge Distillation-based Channel Reduction for Wearable EEG Applications

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posted on 2023-04-25, 13:19 authored by Velu Prabhakar KumaravelVelu Prabhakar Kumaravel, Una Pale, Tomas Teijeiro, Elisabetta FarellaElisabetta Farella, David Atienza Alonso

Wearable EEG applications demand an optimal trade-off between performance and system power consumption. However, high-performing models usually require many features for training and inference, leading to a high computational and memory budget. In this paper, we present a novel knowledge distillation methodology to reduce the number of EEG channels (and therefore, the associated features) without compromising on performance. We aim to distill information from a model trained using all channels (teacher) to a model using a reduced set of channels (student). To this end, we first pre-train the state-of-the-art model on features extracted from all channels. Then, we train a naive model on features extracted from a few task-specific channels using the soft labels predicted by the teacher model. As a result, the student model with a reduced set of features learns to mimic the teacher via soft labels. We evaluate this methodology on two publicly available datasets: CHB-MIT for epileptic seizure detection and BCI competition IV-2a dataset for motor-imagery classification. Results show that the proposed channel reduction methodology improves the precision of the seizure detection task by about 8% and the motor-imagery classification accuracy by about 3.6%. Given these consistent results, we conclude that the proposed framework facilitates future lightweight wearable EEG systems without any degradation in performance.


Email Address of Submitting Author

Submitting Author's Institution

Fondazione Bruno Kessler

Submitting Author's Country

  • Italy