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

Maksimenko, Vladimir A.; Kurkin, Semen A.; Pitsik, Elena; Musatov, Vyacheslav Yu.; Runnova, Anastasiya E.; Efremova, Tatyana Yu.; Hramov, Alexander E.; Pisarchik, Alexander N.

doi:10.1155/2018/9385947

Cited by 111 publications

(54 citation statements)

References 51 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%

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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%

“…Recent progress in this field has been achieved at the junction of mathematics, physics, engineering and neuroscience [1]. This is confirmed by an increasing number of papers related to brain research and published in multidisciplinary journals (see, e.g., [2][3][4][5]).…”

Section: Introductionmentioning

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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“…The multilayer neural network is one of the most popular methods for solving real-time signal processing, system control, and signal prediction [1][2][3] problems. In general, determining the number of hidden levels and neurons for solving a problem is difficult.…”

Section: Introductionmentioning

confidence: 99%

Unstable System Control Using an Improved Particle Swarm Optimization-Based Neural Network Controller

Lin

Jhang

2019

Electronics

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In this study, an improved particle swarm optimization (IPSO)-based neural network controller (NNC) is proposed for solving a real unstable control problem. The proposed IPSO automatically determines an NNC structure by a hierarchical approach and optimizes the parameters of the NNC by chaos particle swarm optimization. The proposed NNC based on an IPSO learning algorithm is used for controlling a practical planetary train-type inverted pendulum system. Experimental results show that the robustness and effectiveness of the proposed NNC based on IPSO are superior to those of other methods.

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“…Ping Jung also used time domain or frequency domain regression [17]. The method removes the eye movement signal, but simply using the regression method introduces new interference into the EEG signal [18].…”

Section: Introductionmentioning

confidence: 99%

Feature recognition of motor imaging EEG signals based on deep learning

Shi

Ren

Cui

2019

Pers Ubiquit Comput

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The brain-computer interface technology interprets the EEG signals displayed by the human brain's neurological thinking activities through computers and instruments, and directly uses the interpreted information to manipulate the outside world, thereby abandoning the human peripheral nerves and muscle systems. The emergence of brain-computer interface technology has brought practical value to many fields. Based on the mechanism and characteristics of motion imaging EEG signals, this paper designs the acquisition experiment of EEG signals. After removing the anomalous samples, the wavelet-reconstruction method is used to extract the specific frequency band of the motion imaging EEG signal. According to the characteristics of motor imagery EEG signals, the feature recognition algorithm of convolutional neural networks is discussed. After an in-depth analysis of the reasons for choosing this algorithm, a variety of different network structures were designed and trained. The optimal network structure is selected by analyzing the experimental results, and the reasons why the structure effect is superior to other structures are analyzed. The results show that the method has a high accuracy rate for the recognition of motor imagery EEG, and it has good robustness.

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

Cited by 111 publications

References 51 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

Unstable System Control Using an Improved Particle Swarm Optimization-Based Neural Network Controller

Feature recognition of motor imaging EEG signals based on deep learning

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