EEG analysis of mathematical cognitive function and startle response using single channel electrode

  • PDF / 1,096,218 Bytes
  • 10 Pages / 595.276 x 790.866 pts Page_size
  • 52 Downloads / 167 Views

DOWNLOAD

REPORT


ORIGINAL RESEARCH

EEG analysis of mathematical cognitive function and startle response using single channel electrode Gopika Gopan K1 • S. V. R. Aditya Reddy1 • Kumaresh Krishnan1 Madhav Rao1 • Neelam Sinha1



Received: 20 March 2019 / Accepted: 26 October 2020 Ó CSI Publications 2020

Abstract Electroencephalographic (EEG) signals are noninvasive means of measuring brain functions. EEG has been used in areas ranging from analysis of neurological disorders, emotional states and sleep pattern to Brain Machine Interface. Here we study cognitive functions from a limited set of daily activities of normal human subjects. For this purpose, we utilize EEG signal obtained from prefrontal cortex, specifically Brodmann Area 10L, which is known to play an important role in these functions. In this work, we study characteristics of a task requiring focussed attention (Mathematical Cognition (T3)), an involuntary response to external stimuli (Startle Response (T2)) and the state of rest (Relax (T1)). The single channel EEG is preprocessed and statistical features are extracted from (i) preprocessed data, (ii) wavelet decomposition, and (iii) cepstral analysis. These features are then separately input to various classifiers. Results are reported for 2 class (T1 vs T2, T1 vs T3, T2 vs T3) and 3 class (T1 vs T2 vs T3) classification framework. Experimental results reveal that cepstral analysis is most effective for classification across both frameworks. In 3-class classification, & Gopika Gopan K [email protected] S. V. R. Aditya Reddy [email protected] Kumaresh Krishnan [email protected] Madhav Rao [email protected] Neelam Sinha [email protected] 1

International Institute of Information Technology, Bangalore, India

cepstral analysis results in a mean accuracy of 96.61% across classifiers. This framework is shown to be effective in classifying the chosen set of cognitive states using EEG and can be extended to broader classes for more conclusive inferences. In addition, gender differences peak at 81.7%, 77.09% and 77.04% for T1, T2 and T3 respectively, indicating that there are significant differences between the genders, although performing the same cognitive task. Keywords EEG  Brodmann Area 10  Classificatio  Statistical analysis

1 Introduction Electrical signals generated in the brain, known as Electroencephalographic (EEG) signals, are considered as one of the non-invasive methods to analyze brain activities. EEG has been used in assessing neurological disorders like epilepsy, Schizophrenia and Parkinson’s disease extensively. In addition, EEG is vastly used in Brain Machine Interface where the user can control assistive movements by mere thoughts. These applications are possible due to the salient characteristics in EEG corresponding to every distinct mental state. EEG analysis involves assessing and exploiting these characteristics to study brain functioning and neurological disorders. EEG analysis is broadly classified into two types based on the absolute EEG signal amplitude, and c