EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces
| العنوان: | EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces |
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| المؤلفون: | Michael Hersche, Nobuaki Kobayashi, Luca Benini, Xiaying Wang, Thorir Mar Ingolfsson, Lukas Cavigelli |
| المساهمون: | Ingolfsson T.M., Hersche M., Wang X., Kobayashi N., Cavigelli L., Benini L. |
| المصدر: | SMC 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) |
| بيانات النشر: | Institute of Electrical and Electronics Engineers Inc., 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, Computer science, Brain–machine interface, Computer Science - Human-Computer Interaction, convolutional neural network, Machine Learning (stat.ML), 02 engineering and technology, Convolutional neural network, Human-Computer Interaction (cs.HC), Machine Learning (cs.LG), 03 medical and health sciences, 0302 clinical medicine, Motor imagery, edge computing, Statistics - Machine Learning, 0202 electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing, business.industry, Deep learning, deep learning, Motor-imagery, Convolutional Neural Networks (CNN), Edge Computing, Pattern recognition, motor-imagery, ComputingMethodologies_PATTERNRECOGNITION, Benchmark (computing), Memory footprint, 020201 artificial intelligence & image processing, Enhanced Data Rates for GSM Evolution, Artificial intelligence, business, brain-machine interface, 030217 neurology & neurosurgery |
| الوصف: | In recent years, deep learning (DL) has contributed significantly to the improvement of motor-imagery brain–machine interfaces (MI-BMIs) based on electroencephalography (EEG). While achieving high classification accuracy, DL models have also grown in size, requiring a vast amount of memory and computational resources. This poses a major challenge to an embedded BMI solution that guarantees user privacy, reduced latency, and low power consumption by processing the data locally. In this paper, we propose EEG-TCNet, a novel temporal convolutional network (TCN) that achieves outstanding accuracy while requiring few trainable parameters. Its low memory footprint and low computational complexity for inference make it suitable for embedded classification on resource-limited devices at the edge. Experimental results on the BCI Competition IV-2a dataset show that EEG-TCNet achieves 77.35% classification accuracy in 4-class MI. By finding the optimal network hyperparameters per subject, we further improve the accuracy to 83.84%. Finally, we demonstrate the versatility of EEG-TCNet on the Mother of All BCI Benchmarks (MOABB), a large scale test benchmark containing 12 different EEG datasets with MI experiments. The results indicate that EEG-TCNet successfully generalizes beyond one single dataset, outperforming the current state-of-the-art (SoA) on MOABB by a meta-effect of 0.25. 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) ISBN:978-1-7281-8527-9 ISBN:978-1-7281-8526-2 |
| وصف الملف: | ELETTRONICO; application/application/pdf |
| اللغة: | English |
| ردمك: | 978-1-72818-527-9 978-1-72818-526-2 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::186b923abecdf6d8c95108b102af4724Test https://hdl.handle.net/11585/800231Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....186b923abecdf6d8c95108b102af4724 |
| قاعدة البيانات: | OpenAIRE |
| ردمك: | 9781728185279 9781728185262 |
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