TY - GEN
T1 - Deep Learning EEG Technology Development and Brain Computer Interface Chips Progress
AU - Li, Wenjie
AU - Chen, Lei
AU - Lv, Bing
AU - Li, Xiaobo
AU - Wang, Weijun
AU - Xu, Kun
AU - Ding, Xilun
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Brain Computer Interfaces establishes an interaction mode between the human brain and external devices, and has broad application prospects. With the development of neuroscience and artificial intelligence technology, brain computer interfaces have rapidly evolved from a single perception stage to a multimodal cognition and complex interaction control stage. This article elaborates on the functional zoning of the brain and cerebellum and the latest research on brain signal classification. Afterwards, the latest research progress of deep learning based EEG signal processing technology was analyzed in depth from three key directions: brain network analysis, research on neurological diseases, cognitive analysis, and emotion recognition. This paper provides a detailed description of the typical structure and main functional unit design ideas of Brain Computer Interface chips, and analyzes the core and difficult technologies from the aspects of acquisition, 3D heterogeneous integration, low-power design, software and brain science research. Finally, based on typical Brain Computer Interface design and application cases, the future development trends of Brain Computer Interface chips and deep learning EEG technology are proposed.
AB - Brain Computer Interfaces establishes an interaction mode between the human brain and external devices, and has broad application prospects. With the development of neuroscience and artificial intelligence technology, brain computer interfaces have rapidly evolved from a single perception stage to a multimodal cognition and complex interaction control stage. This article elaborates on the functional zoning of the brain and cerebellum and the latest research on brain signal classification. Afterwards, the latest research progress of deep learning based EEG signal processing technology was analyzed in depth from three key directions: brain network analysis, research on neurological diseases, cognitive analysis, and emotion recognition. This paper provides a detailed description of the typical structure and main functional unit design ideas of Brain Computer Interface chips, and analyzes the core and difficult technologies from the aspects of acquisition, 3D heterogeneous integration, low-power design, software and brain science research. Finally, based on typical Brain Computer Interface design and application cases, the future development trends of Brain Computer Interface chips and deep learning EEG technology are proposed.
KW - Brain Computer Interface
KW - Deep Learning
KW - Microsystem
UR - http://www.scopus.com/inward/record.url?scp=85202432554&partnerID=8YFLogxK
U2 - 10.1109/ICASSPW62465.2024.10626697
DO - 10.1109/ICASSPW62465.2024.10626697
M3 - Conference contribution
AN - SCOPUS:85202432554
T3 - 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops, ICASSPW 2024 - Proceedings
SP - 413
EP - 418
BT - 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops, ICASSPW 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 49th IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops, ICASSPW 2024
Y2 - 14 April 2024 through 19 April 2024
ER -