The evolution of AI approaches for motor imagery EEG-based BCIs
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Abstract (arXiv)
The Motor Imagery (MI) electroencephalography (EEG) based Brain Computer Interfaces (BCIs) allow the direct communication between humans and machines by exploiting the neural pathways connected to motor imagination. Therefore, these systems open the possibility of developing applications that could span from the medical field to the entertainment industry. In this context, Artificial Intelligence (AI) approaches become of fundamental importance especially when wanting to provide a correct and coherent feedback to BCI users. Moreover, publicly available datasets in the field of MI EEG-based BCIs have been widely exploited to test new techniques from the AI domain. In this work, AI approaches applied to datasets collected in different years and with different devices but with coherent experimental paradigms are investigated with the aim of providing a concise yet sufficiently comprehensive survey on the evolution and influence of AI techniques on MI EEG-based BCI data.
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References · 4
- 2020An Accurate EEGNet-based Motor-Imagery Brain-Computer Interface for Low-Power Edge Computingcited by 16
- 2021EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery Classificationcited by 16
- 2017Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signalscited by 11
- 2017Intent Recognition in Smart Living Through Deep Recurrent Neural Networkscited by 9
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