Deep learning for ECoG brain-Computer interface: end-to-end vs. hand-crafted features
Résumé
In brain signal processing, deep learning (DL) models have become commonly used. However, the performance gain from using end-to-end DL models compared to conventional ML approaches is usually significant but moderate, typically at the cost of increased computational load and deteriorated explainability. The core idea behind deep learning approaches is scaling the performance with bigger datasets. However, brain signals are temporal data with a low signal-to-noise ratio, uncertain labels, and nonstationary data in time. Those factors may influence the training process and slow down the models’ performance improvement. These factors’ influence may differ for end-to-end DL model and one using hand-crafted features.
As not studied before, this paper compares the performance of models that use raw ECoG signals with time-frequency features-based decoders for BCI motor imagery decoding. We investigate whether the current dataset size is a stronger limitation for any models. Finally, obtained filters were compared to identify differences between hand-crafted features and optimized with backpropagation. To compare the effectiveness of both strategies, we used a multilayer perceptron and a mix of convolutional and LSTM layers that were already proved effective in this task. The analysis was performed on the long-term clinical trial database (almost 600 min of recordings over 200 days) of a tetraplegic patient executing motor imagery tasks for 3D hand translation.
For a given dataset, the results showed that end-to-end training might not be significantly better than the hand-crafted features-based model. The performance gap is reduced with bigger datasets, but considering the increased computational load, end-to-end training may not be profitable for this application.
Mots clés
signal processing
spectrum analysis
artificial intelligence
machine learning
deep learning
online learning
brain signal processing
brain signal
explainability
labelling
training
nonstationary data
raw ECoG signal
BCI motor imagery decoding
hand-crafted features
optimization with backpropagation
multilayer perceptron
convolutional layer
LSTM layer
tetraplegic patient
3D hand translation
end-to-end training
ECoG
brain computer interfaces
dataset size
motor imagery
end-to-end
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