Nonlinear analysis of brain activity, associated with motor action and motor imaginary in untrained subjects

Maksimenko, Vladimir A.; Павлов, А. Н.; Runnova, Anastasiya E.; Nedaivozov, Vladimir; Grubov, Vadim V.; Koronovslii, Alexey; Pchelintseva, S. V.; Pitsik, Elena; Pisarchik, Alexander N.; Hramov, Alexander E.

doi:10.1007/s11071-018-4047-y

Cited by 85 publications

(39 citation statements)

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“…It should be noted that frontal lobe is strongly associated with motor activity (e.g., walking), decision making, and many other important cognitive and emotional aspects [30,31]. This result is in agreement with the previous research [19,32], where the time-frequency analysis revealed highly pronounced arm's motor imagery events in event-related desynchronization of delta band in frontal cortex.…”

Section: Resultssupporting

confidence: 92%

“…Finally, similarly to [19], we carried out the timefrequency analysis of motor-related EEGs in order to find brain areas, where the neural dynamics exhibit the most pronounced differences during right and left leg imagery. The time-frequency analysis was performed with the help of wavelet decomposition in three frequency bands: delta (1-5 Hz), alpha (8)(9)(10)(11)(12), and beta (15-30 Hz) (see Material and Methods for detailed description).…”

Section: Resultsmentioning

confidence: 99%

“…Indeed, many BCI studies involve specially trained subjects, since the classification of brain activity patterns during motor imagery of untrained subjects is significantly more difficult and hence poorly studied [18,19]. Although the training is able to increase severity of EEG features and makes the recognition process easier for ANN-based algorithms [20], it cannot be effectively used for patients with motor and mental disabilities [16,21].…”

Section: Introductionmentioning

confidence: 99%

“…The simplest and intuitively clear method for the feature space reduction is a decrease of the number of EEG channels, basing on the time-frequency analysis. In general, the analysis of the time-frequency structure of multichannel EEG allows the brain areas detection, where a significant increase or a decrease in the energy of particular brain rhythms reflects motor activity or motor imagery (event-related synchronization/desynchronization) [19].…”

Section: Introductionmentioning

confidence: 99%

“…Then, we reduce the number of the EEG channels and obtain an appropriate recognition quality (up to 73 ± 15%) using only 8 electrodes located in frontal lobe. Finally, we analyze the time-frequency structure of EEG signals and find that motor-related features associated with left and right leg motor imagery are more pronounced in the mu (8-13 Hz) and delta (1-5 Hz) brainwaves than in the high-frequency beta brainwave (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Based on the obtained results, we propose further ANN optimization by preprocessing the EEG signals with a low-pass filter with different cutoffs.…”

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confidence: 99%

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Artificial Neural Network Classification of Motor‐Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity

et al. 2018

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We apply artificial neural network (ANN) for recognition and classification of electroencephalographic (EEG) patterns associated with motor imagery in untrained subjects. Classification accuracy is optimized by reducing complexity of input experimental data. From multichannel EEG recorded by the set of 31 electrodes arranged according to extended international 10-10 system, we select an appropriate type of ANN which reaches 80 ± 10% accuracy for single trial classification. Then, we reduce the number of the EEG channels and obtain an appropriate recognition quality (up to 73 ± 15%) using only 8 electrodes located in frontal lobe. Finally, we analyze the time-frequency structure of EEG signals and find that motor-related features associated with left and right leg motor imagery are more pronounced in the mu (8-13 Hz) and delta (1-5 Hz) brainwaves than in the high-frequency beta brainwave (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Based on the obtained results, we propose further ANN optimization by preprocessing the EEG signals with a low-pass filter with different cutoffs. We demonstrate that the filtration of high-frequency spectral components significantly enhances the classification performance (up to 90 ± 5% accuracy using 8 electrodes only). The obtained results are of particular interest for the development of brain-computer interfaces for untrained subjects.

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Section: Resultssupporting

confidence: 92%