Cognitive behavior classification from scalp EEG signals

Dino Dvorak, Andrea Shang, Samah Abdel-Baki, Wendy Suzuki, Andre Fenton

Research output: Contribution to journalArticle

Abstract

The electroencephalography (EEG) has become increasingly valuable outside of its traditional use in neurology for neuropsychiatric diagnosis, neurological evaluation of traumatic brain injury, neurotherapy, gaming, neurofeedback, mindfulness and cognitive enhancement training. The trend to increase the number of EEG electrodes, the development of novel analytical methods and the availability of large datasets has created a data analysis challenge to find the “signal of interest” that conveys the most information about ongoing cognitive effort. Accordingly, we compare three common types of neural synchrony measures that are applied to EEG -power analysis, phase locking and phase-amplitude coupling to assess which analytical measure provides the best separation between EEG signals that were recorded while healthy subjects performed eight cognitive tasks -Hopkins Verbal Learning Test and its delayed version, Stroop Test, Symbol Digit Modality Test, Controlled Oral Word Association Test, Trail Marking Test, Digit Span Test and Benton Visual Retention Test. We find that of the three analytical methods, phase-amplitude coupling, specifically theta (4-7 Hz) – high gamma (70-90 Hz) obtained from frontal and parietal EEG electrodes provides both the largest separation between EEG during cognitive tasks and also the highest classification accuracy between pairs of tasks. We also find that phase-locking analysis provides the most distinct clustering of tasks based on their utilization of long-term memory. Finally, we show that phase-amplitude coupling is the least sensitive to contamination by intense jaw-clenching muscle artifact.

Original languageEnglish (US)
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
DOIs
StateAccepted/In press - Jan 23 2018

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Electroencephalography
Scalp
Electrodes
Word Association Tests
Neurofeedback
Stroop Test
Mindfulness
Verbal Learning
Long-Term Memory
Neurology
Jaw
Artifacts
Cluster Analysis
Muscle
Brain
Healthy Volunteers
Contamination
Availability
Data storage equipment
Muscles

Keywords

  • cognition
  • cognition
  • Couplings
  • Electrodes
  • Electroencephalography
  • electroencephalography
  • muscle artifact
  • Muscles
  • Scalp
  • Synchronization
  • synchrony
  • Task analysis

ASJC Scopus subject areas

  • Neuroscience(all)
  • Biomedical Engineering
  • Computer Science Applications

Cite this

Cognitive behavior classification from scalp EEG signals. / Dvorak, Dino; Shang, Andrea; Abdel-Baki, Samah; Suzuki, Wendy; Fenton, Andre.

In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23.01.2018.

Research output: Contribution to journalArticle

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