Stimulation technology for brain and nerves, now and future
원문 초록
In individuals afflicted with conditions such as paralysis, the implementation of Brain-Computer-Interface (BCI) has begun to significantly impact their quality of life. Furthermore, even in healthy individuals, the anticipated advantages of brain-to-brain communication and brain-to-computer interaction hold considerable promise for the future. This is attributed to the liberation from bodily constraints and the transcendence of existing limitations inherent in contemporary brain-to-brain communication methods. To actualize a comprehensive BCI, the establishment of bidirectional communication between the brain and the external environment is imperative. While neural input technology spans diverse disciplines and is currently advancing rapidly, a notable absence exists in the form of review papers summarizing the technology from the standpoint of the latest or potential input methods. The challenges encountered encompass the requisite for bidirectional communication to achieve a holistic BCI, as well as obstacles related to information volume, precision, and invasiveness. The review section comprehensively addresses both invasive and non-invasive techniques, incorporating nanotech/micro-device technology and the integration of Artificial Intelligence (AI) in brain stimulation.
함께 살펴볼 논문
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AGTCNet: A Graph-Temporal Approach for Principled Motor Imagery EEG Classification
Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms
Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification
MVCNet: Multi-View Contrastive Network for Motor Imagery Classification
CSSSTN: A Class-sensitive Subject-to-subject Semantic Style Transfer Network for EEG Classification in RSVP Tasks
Decoding Human Attentive States from Spatial-temporal EEG Patches Using Transformers
토론
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