A 110.3-bits/min 8-ch ssvep-based brain-computer interface soc with 87.9% accuracy

Wooseok Byun, Dokyun Kirn, Sung Yeon Kiin, Ji Hoon Kim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

A wearable brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has been widely studied to enable paralyzed patients to communicate with others. However, target identification accuracy and information transfer rate (JLTK), which are general performance indicators of SSVEP- based BCI system, still need to be further improved in wearable devices. This paper proposes 8-channel SSVEP-based visual target identification system-on-chip (SoC) to improve the ITR of low-cost wearable BCI device while dramatically reducing the computational complexity without accuracy degradation. The proposed target identification algorithm, CCA-CR, includes algorithmic optimizations and candidate reduction (CR) method that reduce signal processing load by at least 75% without degrading target identification accuracy and ITR. This paper also proposes a matrix decomposition processor (MDP) that calculates complex matrix arithmetic operations through systolic array based CCA-CR engines. Compared to the state-of-the-art CCA-based algorithm, the proposed SoC implemented in FPGA exhibits 63% better ITR with 33% reduction of recording time without accuracy degradation.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE Asian Solid-State Circuits Conference, A-SSCC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages201-204
Number of pages4
ISBN (Electronic)9781728151069
DOIs
StatePublished - Nov 2019
Event15th IEEE Asian Solid-State Circuits Conference, A-SSCC 2019 - Macao, China
Duration: 4 Nov 20196 Nov 2019

Publication series

NameProceedings - 2019 IEEE Asian Solid-State Circuits Conference, A-SSCC 2019
Volume2019-November

Conference

Conference15th IEEE Asian Solid-State Circuits Conference, A-SSCC 2019
Country/TerritoryChina
CityMacao
Period4/11/196/11/19

Keywords

  • Brain-computer interface (bci)
  • Canonical correlation analysis (cca)
  • Matrix decomposition
  • Steady-state visual evoked potential (ssvep)
  • System-on-chip
  • Target identification

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