Detrended fluctuation analysis of EEG patterns associated with real and imaginary arm movements

Павлов, А. Н.; Runnova, Anastasiya E.; Maksimenko, Vladimir A.; Pavlova, O. N.; Grishina, Daria S.; Hramov, Alexander E.

doi:10.1016/j.physa.2018.06.096

Cited by 35 publications

(16 citation statements)

References 35 publications

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“…While averaged trials are usually exhibit a clearly pronounced difference between various types of movements (e.g., with left/right hand motor imagery), in the case of single trials, the classification problem is more drastic due to a high variability of EEG or MEG brain signals during imagination, as well as the existence of strong noise. Typically, the classification accuracy does not exceed 80% when special mathematical methods are applied, such as, e.g., SVM machines [55], wavelets [36,56], multilayer perceptrons [4], and recurrence quantitative measures [38].…”

Section: Results Of Real-time Classification Of Brain Activitymentioning

confidence: 99%

Functional Near-Infrared Spectroscopy for the Classification of Motor-Related Brain Activity on the Sensor-Level

Hramov

Grubov

Badarin

et al. 2020

Sensors

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Sensor-level human brain activity is studied during real and imaginary motor execution using functional near-infrared spectroscopy (fNIRS). Blood oxygenation and deoxygenation spatial dynamics exhibit pronounced hemispheric lateralization when performing motor tasks with the left and right hands. This fact allowed us to reveal biomarkers of hemodynamical response of the motor cortex on the motor execution, and use them for designing a sensing method for classification of the type of movement. The recognition accuracy of real movements is close to 100%, while the classification accuracy of imaginary movements is lower but quite high (at the level of 90%). The advantage of the proposed method is its ability to classify real and imaginary movements with sufficiently high efficiency without the need for recalculating parameters. The proposed system can serve as a sensor of motor activity to be used for neurorehabilitation after severe brain injuries, including traumas and strokes. different brain areas (e.g., event-related synchronization/desynchronization) on a sensor level provide important information about the current state of the nervous system and cognitive brain ability [7][8][9][10]. Particular brain states are associated with motor brain activity during either real or imaginary movement.Revealing specific features of spatial brain cortex activity related to real motions and motor imagery of different limbs can be essential not only for basic research in neuroscience, but also for applications in medicine to improve the quality of life of post-traumatic and post-stroke patients using brain-computer interfaces (BCI) for rehabilitation [11][12][13] or to control prostheses and exoskeletons [14]. One of the important BCI functions is online detection of specific features of electromagnetic brain activity using electroencephalography (EEG) [15] or magnetoencephalography (MEG) [16], and transformation of certain patterns into control commands to perform specific actions in the environment without the need of "classical" methods of human-machine interaction [17].Apart from EEG and MEG, other methods are also used to acquire information about brain states. In particular, functional near-infrared spectroscopy (fNIRS) [18,19] is a powerful tool of noninvasive optical imaging successfully used in BCI for registration of brain activity and control command formation [20][21][22]. Control commands for this kind of BCI should not be affected by any muscular activity [23]. Therefore, a study of brain states related to motor imagery is very important for designing such BCI [16,24]. Motor imagery is a mental process by which a person rehearses or simulates a given action with no real motor activity. Some researchers treat motor imagery as a conscious application of unconscious preparation for real motor activity [25]. A number of studies have highlighted common features for real and imaginary motor activity [26][27][28]. One of the common features, important for the BCI development, is that the cortical layout in the primary ...

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Section: Results Of Real-time Classification Of Brain Activitymentioning

confidence: 99%

Functional Near-Infrared Spectroscopy for the Classification of Motor-Related Brain Activity on the Sensor-Level

Hramov

Grubov

Badarin

et al. 2020

Sensors

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“…Our previous studies have revealed the ability to recognize movement types, including mental intentions, based on the scaling exponent

of long-range correlations that can be used, e.g., for brain–computer interfaces [ 38 ]. With these findings, here we performed a windowed DFA within a 2 s floating window (500 samples).…”

Section: Resultsmentioning

confidence: 99%

“…The use of DFA in EEG-based studies allows not only to distinguish between different physiological states (normal and pathological brain dynamics, baseline activity and sudden changes due to external influences, etc. ), but also to characterize the provoked short-term reactions during motor/cognitive tasks when the datasets under study include transients [ 37 , 38 ]. This is the case for brain–computer interfaces which require rapid recognition of specific EEG patterns in order to transform them into control commands for hardware.…”

Section: Introductionmentioning

confidence: 99%

Age-Related Distinctions in EEG Signals during Execution of Motor Tasks Characterized in Terms of Long-Range Correlations

Павлов

Pitsik

Frolov

et al. 2020

Sensors

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The problem of revealing age-related distinctions in multichannel electroencephalograms (EEGs) during the execution of motor tasks in young and elderly adults is addressed herein. Based on the detrended fluctuation analysis (DFA), differences in long-range correlations are considered, emphasizing changes in the scaling exponent α. Stronger responses in elderly subjects are confirmed, including the range and rate of increase in α. Unlike elderly subjects, young adults demonstrated about 2.5 times more pronounced differences between motor task responses with the dominant and non-dominant hand. Knowledge of age-related changes in brain electrical activity is important for understanding consequences of healthy aging and distinguishing them from pathological changes associated with brain diseases. Besides diagnosing age-related effects, the potential of DFA can also be used in the field of brain–computer interfaces.

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“…Traditionally, ERD detection is done via time-frequency analysis [Wang et al, 2004;Ince et al, 2007;Maksimenko et al, 2018b] with the decrease of spectral power density as a classification criteria [Carrera-Leon et al, 2012;Xu and Song, 2008]. Besides, various methods were applied for this purpose including spatial filtering [Wang et al, 2006], detrended fluctuation analysis [Pavlov et al, 2018;Pavlov et al, 2019], clasterization methods [Chholak et al, 2019], and artificial intelligence [Sakhavi et al, 2015;Grubov et al, 2017;Maksimenko et al, 2018a].…”

Section: Introductionmentioning

confidence: 99%

Recurrence plot structures reflect motor-related EEG pattern

Pitsik

Frolov

2019

CAP

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Detection and classification of motor-related brain patterns from non-invasive electroencephalograms (EEGs) is challenging due to their non-stationarity and low signal-to-noise ratio and requires using advanced mathematical approaches. Traditionally applied methods such as time-frequency analysis and spatial filtering allow to quantify the main attribute of the motor-related brain activity – contralateral desynchronization of mu-band oscillations (8-13 Hz) in sensorimotor cortex – by measuring EEG signal’s amplitude, power spectral density, location etc. However, these features suffer from strong inter- and intra-subject variability. So, special attention is paid to the finding of stable features. In present paper, we investigate application of the recurrence plots – robust mathematical tool for nonstationary data analysis – to explore properties of motor-related EEG samples. Our goal is to show that recurrence plots are sensitive to the changes in brain activity accessed from noninvasive EEG recordings and may provide us a new context for interpretation of motor-related pattern in EEG.

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Detrended fluctuation analysis of EEG patterns associated with real and imaginary arm movements

Cited by 35 publications

References 35 publications

Functional Near-Infrared Spectroscopy for the Classification of Motor-Related Brain Activity on the Sensor-Level

Functional Near-Infrared Spectroscopy for the Classification of Motor-Related Brain Activity on the Sensor-Level

Age-Related Distinctions in EEG Signals during Execution of Motor Tasks Characterized in Terms of Long-Range Correlations

Recurrence plot structures reflect motor-related EEG pattern

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