Electroencephalograms during Mental Arithmetic Task Performance

Abstract

This work has been carried out to support the investigation of the electroencephalogram (EEG) Fourier power spectral, coherence, and detrended fluctuation characteristics during performance of mental tasks. To this aim, the presented dataset contains International 10/20 system EEG recordings from subjects under mental cognitive workload (performing mental serial subtraction) and the corresponding reference background EEGs. Based on the subtraction task performance (number of subtractions and accuracy of the result), the subjects were divided into good counters and bad counters (for whom the mental task required excessive efforts). The data was recorded from 36 healthy volunteers of matched age, all of whom are students of Educational and Scientific Centre “Institute of Biology and Medicine”, National Taras Shevchenko University of Kyiv (Ukraine); the recordings are available through Physiobank platform. The dataset can be used by the neuroscience research community studying brain dynamics during cognitive workload.

Pattern Recognition of Cognitive Load Using EEG and ECG Signals

“…The dataset used in this work is available on Physiobank, and has been contributed by Igor Zyma, Sergii Tukaev, and Ivan Seleznov [ 27 , 28 ]. Firstly, necessary information about this dataset, e.g., subjects and mental task procedure, is introduced in this section.…”

“…The mental arithmetic task for inducing cognitive load was to continuously subtract the two-digit number from the four-digit number, and the accurate calculation times of each subject in four minutes were counted out after the arithmetic task. The subjects were rated according to the number of accurate calculations, and they were divided into two groups [ 27 , 28 ]. Group “G” had 24 subjects who performed good quality count (number of accurate calculations in 4 min: 21 ± 7.4); Group “B” had 12 subjects who performed bad quality count (number of accurate calculations in 4 min: 7 ± 3.6) [ 27 , 28 ].…”

“…The subjects were rated according to the number of accurate calculations, and they were divided into two groups [ 27 , 28 ]. Group “G” had 24 subjects who performed good quality count (number of accurate calculations in 4 min: 21 ± 7.4); Group “B” had 12 subjects who performed bad quality count (number of accurate calculations in 4 min: 7 ± 3.6) [ 27 , 28 ]. Before the 4-min mental arithmetic task began, there were 3 min of baseline state recorded from each subject.…”

“…Before the 4-min mental arithmetic task began, there were 3 min of baseline state recorded from each subject. ECG and EEG data were acquired throughout the experiment, but only the 3-min baseline data and the data in the first-minute calculation were kept in the dataset [ 27 , 28 ].…”

Low Complexity Automatic Stationary Wavelet Transform for Elimination of Eye Blinks from EEG

“…We manually cut out 10 s long artifact-free EEG epochs, and in this way, we were able to collect pure EEG signals. EEG signals collected during mental arithmetic tasks (EEG-MAT) (Fs = 500 Hz) [44,46] have been selected to test the performance of the proposed algorithm. EEG-MAT database had already been filtered and the data was clean.…”