Classification of Motor Imagery Based EEG Signals Using Sparsity Approach

Sreeja, S. R.; Rabha, Joytirmoy; Samanta, Debasis; Mitra, Pabitra; Sarma, Monalisa

doi:10.1007/978-3-319-72038-8_5

Cited by 19 publications

(13 citation statements)

References 24 publications

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“…Several studies are performed to use a selected number of channels to design the MI-BCI [29]- [31]. The 18 channels from the area of the sensorimotor cortex are used in [29], while 30 channels are selected in [30], [31] to classify two MI tasks. Considering the previous studies [30], [31], 30 channels in the sensory-motor cortex area are selected for MI task classification, as indicated in Fig.…”

Section: A Bci Competition III Dataset 4amentioning

confidence: 99%

Discriminative Feature Selection-Based Motor Imagery Classification Using EEG Signal

et al. 2020

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Achieving a reliable classification of motor imagery (MI) tasks is a major challenge in brain-computer interface (BCI) implementation. The set of relevant and discriminative features plays an important role in the classification scheme. This paper presents a supervised approach to select discriminative features for the enhancement of MI classification using multichannel electroencephalography (EEG) signal. The dimension of multiband feature space is reduced using the feature selection method. Each trial of the multichannel EEG signal representing MI tasks is decomposed into a finite set of narrowband signals. The common spatial pattern-based features are extracted from each subband. The features obtained from the multiple subbands are combined to derive a high-dimensional feature vector. The neighborhood component analysis-based feature selection method is implemented to select the features that are relevant in performing an accurate classification. It is a nearest-neighbor-based approach to learn the feature weights with regularization by maximizing the average leave-one-out classification accuracy over the labeled training data. The selected features are used to train the support vector machine for classification. The features relatively irrelevant to the classification task are discarded, yielding a reduction of feature dimension. The evaluation of the proposed method is performed using BCI Competition III dataset 4a and IV dataset 2b. Both are publicly available datasets and are used as types of benchmark data to evaluate the MI classification algorithm to implement BCI. The obtained simulation results confirm the superiority of the proposed method compared to the recently developed algorithms.INDEX TERMS Brain-computer interface (BCI), electroencephalography (EEG), machine learning, motor imagery (MI), subband decomposition, supervised feature selection.

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Section: A Bci Competition III Dataset 4amentioning

confidence: 99%

Discriminative Feature Selection-Based Motor Imagery Classification Using EEG Signal

et al. 2020

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“…In [6] a general architecture for the motor imagery signal classification for BCI devices is presented. [14] have proposed an efficient EEG classification technique to address the issue of inter-subject variability. Similar work in the field of hand movement classification using motor imagery based EEG signals also exists [16,18,19].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Action Oriented Effects on Perceptual Process of Object Recognition Using Physiological Responses

Sharma¹,

Mishra²,

Kumar

et al. 2018

Intelligent Human Computer Interaction

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“…Initially, for data pre-processing, we applied a zero-phase fourth-order Butterworth filter for band-pass signal filtering in the EEG signals. The data were filtered between 8 and 30 Hz (μ and β bands, respectively), known to be within the motor-related frequency range, and this could also include the spectral range for somatosensory rhythm observation (i.e., 13–15 Hz) [ 40 , 41 ]. For artifact rejection, the apparent eye-blinking contamination in the EEG signal was removed via independent component analysis (ICA) [ 42 ].…”

Section: Data Validationmentioning

confidence: 99%

Multimodal signal dataset for 11 intuitive movement tasks from single upper extremity during multiple recording sessions

et al. 2020

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Background Non-invasive brain–computer interfaces (BCIs) have been developed for realizing natural bi-directional interaction between users and external robotic systems. However, the communication between users and BCI systems through artificial matching is a critical issue. Recently, BCIs have been developed to adopt intuitive decoding, which is the key to solving several problems such as a small number of classes and manually matching BCI commands with device control. Unfortunately, the advances in this area have been slow owing to the lack of large and uniform datasets. This study provides a large intuitive dataset for 11 different upper extremity movement tasks obtained during multiple recording sessions. The dataset includes 60-channel electroencephalography, 7-channel electromyography, and 4-channel electro-oculography of 25 healthy participants collected over 3-day sessions for a total of 82,500 trials across all the participants. Findings We validated our dataset via neurophysiological analysis. We observed clear sensorimotor de-/activation and spatial distribution related to real-movement and motor imagery, respectively. Furthermore, we demonstrated the consistency of the dataset by evaluating the classification performance of each session using a baseline machine learning method. Conclusions The dataset includes the data of multiple recording sessions, various classes within the single upper extremity, and multimodal signals. This work can be used to (i) compare the brain activities associated with real movement and imagination, (ii) improve the decoding performance, and (iii) analyze the differences among recording sessions. Hence, this study, as a Data Note, has focused on collecting data required for further advances in the BCI technology.

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Classification of Motor Imagery Based EEG Signals Using Sparsity Approach

Cited by 19 publications

References 24 publications

Discriminative Feature Selection-Based Motor Imagery Classification Using EEG Signal

Discriminative Feature Selection-Based Motor Imagery Classification Using EEG Signal

Analysis of Action Oriented Effects on Perceptual Process of Object Recognition Using Physiological Responses

Multimodal signal dataset for 11 intuitive movement tasks from single upper extremity during multiple recording sessions

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