Open AccessHigh-density mapping of primate digit representations with a 1152-channel µECoG array
Author(s) -
Taro Kaiju,
Masato Inoue,
Masayuki Hirata,
Takafumi Suzuki
Publication year - 2021
Publication title -
journal of neural engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.594
H-Index - 111
eISSN - 1741-2560
pISSN - 1741-2552
DOI - 10.1088/1741-2552/abe245
Subject(s) - computer science , channel (broadcasting) , signal (programming language) , electrode array , electrophysiology , computer hardware , pattern recognition (psychology) , artificial intelligence , electrode , neuroscience , telecommunications , physics , quantum mechanics , biology , programming language
Objective. Advances in brain–machine interfaces (BMIs) are expected to support patients with movement disorders. Electrocorticogram (ECoG) measures electrophysiological activities over a large area using a low-invasive flexible sheet placed on the cortex. ECoG has been considered as a feasible signal source of the clinical BMI device. To capture neural activities more precisely, the feasibility of higher-density arrays has been investigated. However, currently, the number of electrodes is limited to approximately 300 due to wiring difficulties, device size, and system costs. Approach. We developed a high-density recording system with a large coverage (14 × 7 mm 2 ) and using 1152 electrodes by directly integrating dedicated flexible arrays with the neural-recording application-specific integrated circuits and their interposers. Main results. Comparative experiments with a 128-channel array demonstrated that the proposed device could delineate the entire digit representation of a nonhuman primate. Subsampling analysis revealed that higher-amplitude signals can be measured using higher-density arrays. Significance. We expect that the proposed system that simultaneously establishes large-scale sampling, high temporal-precision of electrophysiology, and high spatial resolution comparable to optical imaging will be suitable for next-generation brain-sensing technology.