Comparison of Classification Methods for EEG Signals of Real and Imaginary Motion

Szczuko, Piotr; Lech, Michał; Czyżewski, Andrzej

doi:10.1007/978-3-319-67588-6_12

Cited by 9 publications

(11 citation statements)

References 46 publications

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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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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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“…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%

“…Several conventional machine learning methods have been employed using this approach, for instance, in [10], an average accuracy of 64.02% was reported using a Common Spatial Patterns (CSP) approach for 10 subjects. In [11], an average accuracy of 88.69% was reported using QDA, while a rough set-based classifier was used in [12] and [13], reporting average accuracies of 60% and 68% respectively. However, the aforementioned traditional classifiers often cannot model the non-linearities observed in high-dimensional multi-channel EEG and featuresets extracted from the data.…”

Section: Related Workmentioning

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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“…Analogous to the best feature subset identification, there are also many works in the literature comparing classifiers (see e.g., [11][12][13]), but few present a systematic quantitative comparison of features [14] and only in [9,10] were the EMG signals obtained from amputees. More importantly, detecting intended applied force has received little attention; with most studies relying on subjects with intact limbs.…”

Section: Introductionmentioning

confidence: 99%

Regulating Grip Forces through EMG-Controlled Protheses for Transradial Amputees

Rasines

Prada²,

Bobrov³

et al. 2021

Applied Sciences

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This study aims to evaluate different combinations of features and algorithms to be used in the control of a prosthetic hand wherein both the configuration of the fingers and the gripping forces can be controlled. This requires identifying machine learning algorithms and feature sets to detect both intended force variation and hand gestures in EMG signals recorded from upper-limb amputees. However, despite the decades of research into pattern recognition techniques, each new problem requires researchers to find a suitable classification algorithm, as there is no such thing as a universal ’best’ solution. Consideration of different techniques and data representation represents a fundamental practice in order to achieve maximally effective results. To this end, we employ a publicly-available database recorded from amputees to evaluate different combinations of features and classifiers. Analysis of data from 9 different individuals shows that both for classic features and for time-dependent power spectrum descriptors (TD-PSD) the proposed logarithmically scaled version of the current window plus previous window achieves the highest classification accuracy. Using linear discriminant analysis (LDA) as a classifier and applying a majority-voting strategy to stabilize the individual window classification, we obtain 88% accuracy with classic features and 89% with TD-PSD features.

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Comparison of Classification Methods for EEG Signals of Real and Imaginary Motion

Cited by 9 publications

References 46 publications

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

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

Regulating Grip Forces through EMG-Controlled Protheses for Transradial Amputees

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