Influence of Frequency Bands in EEG Signal to Predict User Intent

Melynda A. Schreiber, Mitja Trkov, Andrew Merryweather

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Decoding motor user intent using electroencephalography (EEG) sensors may aid individuals with limited upper extremity mobility. Researchers have successfully used Filter Bank Common Spatial Pattern (FBCSP) and Linear Discriminant Analysis (LDA) to predict user intent from EEG. Frequency bands between 8 to 40 Hz, with 4 Hz intervals are used as inputs to FBCSP classification. While this range includes the commonly used alpha (8-13 Hz) and beta (16-24 Hz) frequency bands, it excludes the delta band (0.3-3 Hz). The delta band has been shown to contain information regarding user intent. This paper investigates the accuracy of predicting user intent to reach and lift when modifying FBCSP to include combinations of alpha, beta and delta bands. This comparison was performed for clinical (32-electrodes) and commercial (12-electrodes) EEG electrode configurations using the WAY-EEG-GAL dataset. The simulated commercial configuration placement mimicked those from the Emotiv Epoc+. We predicted intent to reach (HandStart) and lift a block (LiftOff) by comparing the EEG signal to rest conditions. Intent classification accuracy is the accuracy to predict the future event before the event occurs. Maximum classification accuracy is the maximum accuracy to predict the event during the region of interest (-2.5, -2.0 sec.). Intent classification accuracy of 80.2% and 76.7% were achieved for the clinical and commercial configurations, respectively. Maximum classification accuracy of 85.9% and 83.0% were achieved for the clinical and commercial configuration, respectively. The combination of all three bands outperformed alpha-beta-only and delta-only by 0.9% and 9.9% for the clinical and 4.0% and 4.3% for the commercial for maximum classification accuracy. Using the combination of bands resulted in a maximum classification above 80% for the commercial configuration. These results suggest that including the delta frequency band can help improve the accuracy of user intent in clinical and commercial headsets.

Original languageEnglish (US)
Title of host publication9th International IEEE EMBS Conference on Neural Engineering, NER 2019
PublisherIEEE Computer Society
Pages1126-1129
Number of pages4
ISBN (Electronic)9781538679210
DOIs
StatePublished - May 16 2019
Externally publishedYes
Event9th International IEEE EMBS Conference on Neural Engineering, NER 2019 - San Francisco, United States
Duration: Mar 20 2019Mar 23 2019

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
Volume2019-March
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Conference

Conference9th International IEEE EMBS Conference on Neural Engineering, NER 2019
Country/TerritoryUnited States
CitySan Francisco
Period3/20/193/23/19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Mechanical Engineering

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