Real and imaginary motion classification based on rough set analysis of EEG signals for multimedia applications

The purpose of this paper is to record and analyze induced gamma-band activity (GBA) (30–60 Hz) in cerebral motor areas during imaginary movement and to compare it quantitatively with activity recorded in the same areas during actual movement using a simplified electroencephalogram (EEG). Brain activity (basal activity, imaginary motor task and actual motor task) is obtained from 12 healthy volunteer subjects using an EEG (Cz channel). GBA is analyzed using the mean power spectral density (PSD) value. Event-related synchronization (ERS) is calculated from the PSD values of the basal GBA (GBAb), the GBA of the imaginary movement (GBAim) and the GBA of the actual movement (GBAac). The mean GBAim and GBAac values for the right and left hands are significantly higher than the GBAb value (p = 0.007). No significant difference is detected between mean GBA values during the imaginary and actual movement (p = 0.242). The mean ERS values for the imaginary movement (ERSimM (%) = 23.52) and for the actual movement (ERSacM = 27.47) do not present any significant difference (p = 0.117). We demonstrated that ERS could provide a useful way of indirectly checking the function of neuronal motor circuits activated by voluntary movement, both imaginary and actual. These results, as a proof of concept, could be applied to physiology studies, brain–computer interfaces, and diagnosis of cognitive or motor pathologies.

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“…Some BCI´s multichannel works that analyze the gamma band can be referenced: [40] (19 EEG channels) [41] (21 channels), or [42] (128 channels).…”

Section: Introductionmentioning

confidence: 99%

Induced Gamma-Band Activity during Actual and Imaginary Movements: EEG Analysis

Usanos

Boquete

Santiago

et al. 2020

Sensors

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“…A series of tests with a larger group of participants with varying severity of ABI is planned. These tests will permit the investigation of the structure of mental states detected by the algorithm in the context of personal differences associated with the ability to use BCI [19][20][21]. It would also be worth investigating how the pattern and presence of rarely observed signal detected correlate with the effectiveness of using BCI and if there are participants for whom such a method of monitoring brain activity will not work.…”

Section: Discussionmentioning

confidence: 99%

Multifactor consciousness level assessment of participants with acquired brain injuries employing human–computer interfaces

et al. 2020

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Background: A lack of communication with people suffering from acquired brain injuries may lead to drawing erroneous conclusions regarding the diagnosis or therapy of patients. Information technology and neuroscience make it possible to enhance the diagnostic and rehabilitation process of patients with traumatic brain injury or posthypoxia. In this paper, we present a new method for evaluation possibility of communication and the assessment of such patients' state employing future generation computers extended with advanced human-machine interfaces.Methods: First, the hearing abilities of 33 participants in the state of coma were evaluated using auditory brainstem response measurements (ABR). Next, a series of interactive computer-based exercise sessions were performed with the therapist's assistance. Participants' actions were monitored with an eye-gaze tracking (EGT) device and with an electroencephalogram EEG monitoring headset. The data gathered were processed with the use of data clustering techniques.Results: Analysis showed that the data gathered and the computer-based methods developed for their processing are suitable for evaluating the participants' responses to stimuli. Parameters obtained from EEG signals and eye-tracker data were correlated with Glasgow Coma Scale (GCS) scores and enabled separation between GCS-related classes. The results show that in the EEG and eye-tracker signals, there are specific consciousness-related states discoverable. We observe them as outliers in diagrams on the decision space generated by the autoencoder. For this reason, the numerical variable that separates particular groups of people with the same GCS is the variance of the distance of points from the cluster center that the autoencoder generates. The higher the GCS score, the greater the variance in most cases. The results proved to be statistically significant in this context. Conclusions:The results indicate that the method proposed may help to assess the consciousness state of participants in an objective manner.

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“…Accuracy˘SD Precision˘SD Recall˘SD PLV [15] 78.9´Á NN [16] 68 our proposed method with solutions from previous studies namely CSP [10], QDA [11], rough set-based [12], [13], and MDA [14] described in Section II. We did not attempt to utilize more benchmarking solutions in this scheme, as most of the related works in this area utilize intra-subject validation, thus comparing to those works was deemed sufficient.…”

Section: Methodsmentioning

confidence: 99%

Classification of Hand Movements From EEG Using a Deep Attention-Based LSTM Network

et al. 2020

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Classifying limb movements using brain activity is an important task in Brain-computer Interfaces (BCI) that has been successfully used in multiple application domains, ranging from human-computer interaction to medical and biomedical applications. This paper proposes a novel solution for classification of left/right hand movement by exploiting a Long Short-Term Memory (LSTM) network with attention mechanism to learn the electroencephalogram (EEG) time-series information. To this end, a wide range of time and frequency domain features are extracted from the EEG signals and used to train an LSTM network to perform the classification task. We conduct extensive experiments with the EEG Movement dataset and show that our proposed solution our method achieves improvements over several benchmarks and state-of-the-art methods in both intrasubject and cross-subject validation schemes. Moreover, we utilize the proposed framework to analyze the information as received by the sensors and monitor the activated regions of the brain by tracking EEG topography throughout the experiments.

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Real and imaginary motion classification based on rough set analysis of EEG signals for multimedia applications

Cited by 21 publications

References 49 publications

Induced Gamma-Band Activity during Actual and Imaginary Movements: EEG Analysis

Induced Gamma-Band Activity during Actual and Imaginary Movements: EEG Analysis

Multifactor consciousness level assessment of participants with acquired brain injuries employing human–computer interfaces

Classification of Hand Movements From EEG Using a Deep Attention-Based LSTM Network

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