Key result
Novel lightweight deep learning model achieves up to ~97% accuracy on EEG motor imagery tasks.
Why the study?
Increasing deep learning model size and computational demands make them difficult to embed and execute on IoT, cloud, or edge devices used in rehabilitation.
A novel lightweight deep learning model combining attention-inception CNN and LSTM achieves high accuracy for EEG-based motor imagery classification with low computational requirements.
May advance efficient BCI systems for motor imagery; hypothesis-generating and requires prospective clinical validation.
In recent years, the contributions of deep learning have had a phenomenal impact on electroencephalography-based brain-computer interfaces. While the decoding accuracy of electroencephalography signals has continued to increase, the process has caused deep learning models to continuously expand in terms of size and computational resource requirements. However, due to their increased size and computational requirements, it has become difficult to embed, store, and execute deep learning models for artificial intelligence of things, cloud-based, or edge devices used in rehabilitation. Hence, this article proposes a novel deep learning-based lightweight model based on attention-inception convolutional neural network and long- short-term memory. The proposed model achieves excellent accuracy on public competition datasets while requiring few parameters and low computational time. Using the BCI competition IV 2a dataset and the high gamma dataset, the proposed model achieved 82.8% and 97.1% accuracies, respectively.
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Amin et al. (2021) studied Motor imagery tasks in rehabilitation. Attention-inception convolutional neural network and long- short-term memory model was evaluated on Classification accuracy. A novel lightweight deep learning model based on attention-inception CNN and LSTM achieved classification accuracies of 82.8% and 97.1% on public EEG datasets for motor imagery tasks.
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