Preprint Article Version 1 NOT YET PEER-REVIEWED

Visual Motion Onset Brain-Computer Interface

  1. University of Tsukuba, Tsukuba, Japan
  2. Federal University of ABC (UFABC), Santo Andre, Sao Paulo, Brazil
  3. University of Campinas (UNICAMP), Campinas, Sao Paulo, Brazil
  4. Current address: The University of Tokyo, Tokyo, Japan
Version 1 : Received: 30 September 2016 / Approved: 30 September 2016 / Online: 30 September 2016 (14:46:39 CEST)
Version 2 : Received: 3 October 2016 / Approved: 4 October 2016 / Online: 4 October 2016 (14:43:48 CEST)

How to cite: Peraira Junior, J.; Teixeira, C.; Rutkowski, T. Visual Motion Onset Brain-Computer Interface. Preprints 2016, 2016090126 (doi: 10.20944/preprints201609.0126.v1). Peraira Junior, J.; Teixeira, C.; Rutkowski, T. Visual Motion Onset Brain-Computer Interface. Preprints 2016, 2016090126 (doi: 10.20944/preprints201609.0126.v1).

Abstract

The paper presents a study of two novel visual motion onset stimulus-based brain–computer interfaces (vmoBCI). Two settings are compared with afferent and efferent to a computer screen center motion patterns. Online vmoBCI experiments are conducted in an oddball event–related potential (ERP) paradigm allowing for “aha–responses” decoding in EEG brainwaves. A subsequent stepwise linear discriminant analysis classification (swLDA) classification accuracy comparison is discussed based on two inter–stimulus–interval (ISI) settings of 700 and 150 ms in two online vmoBCI applications with six and eight command settings. A research hypothesis of classification accuracy non–significant differences with various ISIs is confirmed based on the two settings of 700 ms and 150 ms, as well as with various numbers of ERP response averaging scenarios.The efferent in respect to display center visual motion patterns allowed for a faster interfacing and thus they are recommended as more suitable for the no–eye–movements requiring visual BCIs.

Subject Areas

Brain-computer interface (BCI); visual motion perception; neurotechnology application; EEG; realtime brain signal decoding

Readers' Comments and Ratings (0)

Discuss and rate this article
Views 122
Downloads 108
Comments 0
Metrics 0
Discuss and rate this article

×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.