Next Article in Journal
Classification of Drowsiness Levels Based on a Deep Spatio-Temporal Convolutional Bidirectional LSTM Network Using Electroencephalography Signals
Previous Article in Journal
Higher Serum Melatonin Levels during the First Week of Malignant Middle Cerebral Artery Infarction in Non-Surviving Patients
Previous Article in Special Issue
Exploring Shopper’s Browsing Behavior and Attention Level with an EEG Biosensor Cap
Open AccessArticle

Peak Detection with Online Electroencephalography (EEG) Artifact Removal for Brain–Computer Interface (BCI) Purposes

Faculty of Technology and Bionics, Rhine-Waal University of Applied Sciences, 47533 Kleve, Germany
*
Author to whom correspondence should be addressed.
Brain Sci. 2019, 9(12), 347; https://doi.org/10.3390/brainsci9120347
Received: 23 October 2019 / Revised: 19 November 2019 / Accepted: 25 November 2019 / Published: 29 November 2019
Brain–computer interfaces (BCIs) measure brain activity and translate it to control computer programs or external devices. However, the activity generated by the BCI makes measurements for objective fatigue evaluation very difficult, and the situation is further complicated due to different movement artefacts. The BCI performance could be increased if an online method existed to measure the fatigue objectively and accurately. While BCI-users are moving, a novel automatic online artefact removal technique is used to filter out these movement artefacts. The effects of this filter on BCI performance and mainly on peak frequency detection during BCI use were investigated in this paper. A successful peak alpha frequency measurement can lead to more accurately determining objective user fatigue. Fifteen subjects performed various imaginary and actual movements in separate tasks, while fourteen electroencephalography (EEG) electrodes were used. Afterwards, a steady-state visual evoked potential (SSVEP)-based BCI speller was used, and the users were instructed to perform various movements. An offline curve fitting method was used for alpha peak detection to assess the effect of the artefact filtering. Peak detection was improved by the filter, by finding 10.91% and 9.68% more alpha peaks during simple EEG recordings and BCI use, respectively. As expected, BCI performance deteriorated from movements, and also from artefact removal. Average information transfer rates (ITRs) were 20.27 bit/min, 16.96 bit/min, and 14.14 bit/min for the (1) movement-free, (2) the moving and unfiltered, and (3) the moving and filtered scenarios, respectively. View Full-Text
Keywords: Brain-Computer Interface (BCI); Steady-State Visual Evoked Potential (SSVEP); artefact removal; Individual Alpha Peak; movement artefact; Electroencephalography (EEG) Brain-Computer Interface (BCI); Steady-State Visual Evoked Potential (SSVEP); artefact removal; Individual Alpha Peak; movement artefact; Electroencephalography (EEG)
Show Figures

Figure 1

MDPI and ACS Style

Benda, M.; Volosyak, I. Peak Detection with Online Electroencephalography (EEG) Artifact Removal for Brain–Computer Interface (BCI) Purposes. Brain Sci. 2019, 9, 347.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop