Sub-Scalp Brain-Computer Interface Device Design and Fabrication
원문 초록
Current brain-computer interfaces (BCI) face limitations in signal acquisition. While sub-scalp EEG offers a potential solution, existing devices prioritize chronic seizure monitoring and lack features suited for BCI applications. This work addresses this gap by outlining key specifications for sub-scalp BCI devices, focusing on channel count, sampling rate, power efficiency, and form factor. We present the Set-And-Forget EEG (SAFE) system, a custom-built amplifier and wireless transmitter meeting these criteria. This compact (12x12 mm), six-channel device offers 1024 Hz sampling and Bluetooth Low Energy data transmission. Validation using generated sinusoids and electrocorticography recordings of visual evoked potentials in sheep models demonstrated low noise recording. Future animal studies will assess sub-scalp EEG signal quality for BCI applications. This data lays the groundwork for human trials, ultimately paving the way for chronic, in-home BCIs that empower individuals with physical disabilities.
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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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