Skip to main content

Hybrid BCI utilising SSVEP and P300 event markers for reliable and improved classification using LED stimuli

Mouli, S., Palaniappan, Ramaswamy (2017) Hybrid BCI utilising SSVEP and P300 event markers for reliable and improved classification using LED stimuli. In: 2017 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE). . pp. 127-131. IEEE ISBN 978-1-5090-4752-9. (doi:10.1109/ISCAIE.2017.8074963) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1109/ISCAIE.2017.8074963

Abstract

This paper investigates the possibilities of developing a hybrid brain-computer interface based on Steady State Visual Evoked Potential (SSVEP) and P300 responses. SSVEP classification accuracy is improved using P300 event detection as a secondary validation technique in this study. SSVEP events are generated using a hybrid visual stimuli consisting of four independent radial chip-on-board green LED rings flashing at frequencies 7, 8 9 and 10 Hz, which are controlled by four 32-bit microcontrollers to ensure precise generation of flashing frequencies. P300 events are generated with a flash stimulus controller that produces random red LED flashes using high power single LED located inside each of the four radial rings. The P300 flashes are marked as events along with the recorded SSVEP EEG. The study analysed the EEG data recorded from five participants comprising of five trials each, which included both SSVEP and P300 events to identify the classification effectiveness for hybrid BCI. The EEG data was band-pass filtered and events extracted using custom MATLAB algorithms showed that SSVEP classifications could be improved using P300 events for reliable BCI applications. © 2017 IEEE.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1109/ISCAIE.2017.8074963
Additional information: Unmapped bibliographic data: C7 - 8074963 [EPrints field already has value set] LA - English [Field not mapped to EPrints] J2 - ISCAIE - IEEE Symp. Comput. Appl. Ind. Electron. [Field not mapped to EPrints] AD - Data Science (E-Health) Research Group, School of Computing, University of Kent, United Kingdom [Field not mapped to EPrints] DB - Scopus [Field not mapped to EPrints] M3 - Conference Paper [Field not mapped to EPrints] A4 - IEEE Malaysia Industrial Electronics and Industrial Applications Joint Chapter [Field not mapped to EPrints] C3 - ISCAIE 2017 - 2017 IEEE Symposium on Computer Applications and Industrial Electronics [Field not mapped to EPrints]
Uncontrolled keywords: Brain-computer Interface, EMOTIV, Hybrid BCI, LED stimulus, P300, Radial Stimulus, SSVEP, Visual Fatigue, Industrial electronics, Interface states, Interfaces (computer), Light emitting diodes, EMOTIV, Hybrid bci, P300, Radial Stimulus, SSVEP, Visual fatigue, Brain computer interface
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 14 Dec 2018 18:13 UTC
Last Modified: 30 May 2019 08:28 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/70674 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
  • Depositors only (login required):