Multiclass common spatial pattern with artifacts removal methodology for EEG signals

Bhatti

et al. 2019

J Wireless Com Network

Electroencephalogram (EEG) signal classification plays an important role to facilitate physically impaired patients by providing brain-computer interface (BCI)-controlled devices. However, practical applications of BCI make it difficult to decode motor imagery-based brain signals for multiclass classification due to their non-stationary nature. In this study, we aim to improve multiclass classification accuracy for motor imagery movement using sub-band common spatial patterns with sequential feature selection (SBCSP-SBFS) method. Filter bank having bandpass filters of different overlapped frequency cutoffs is applied to suppress the noise signals from raw EEG signals. The output of these sub-band filters is sent for feature extraction by applying common spatial pattern (CSP) and linear discriminant analysis (LDA). As all of the extracted features are not necessary for classification therefore, selection of optimal features is done by passing the extracted features to sequential backward floating selection (SBFS) technique. Three different classifiers were then trained on these optimal features, i.e., support vector machine (SVM), Naïve-Bayesian Parzen-Window (NBPW), and k-Nearest Neighbor (KNN). Results are evaluated on two datasets, i.e., Emotiv Epoc and wet gel electrodes for three classes, i.e., right-hand motor imagery, left hand motor imagery, and rest state. The proposed model yields a maximum accuracy of 60.61% in case of Emotiv Epoc headset and 86.50% for wet gel electrodes. The computed accuracy shows an increase of 7% as compared to previously implemented multiclass EEG classification.

Section: Related Workmentioning

confidence: 99%

Multiclass EEG motor-imagery classification with sub-band common spatial patterns

Bhatti

et al. 2019

J Wireless Com Network

“…The common spatial pattern (CSP) feature extraction was initially applied in the classification of motor imagery EEG [14,15,16,17,18,19,20,21]. The CSP-based algorithms can effectively discriminate two different events by minimizing the variance of one class while maximizing the variance of another class, giving better classification accuracy [17,22].…”

Section: Related Workmentioning

confidence: 99%

“…In most cases, CSP-based methods are limited to two-class classification of EEG data since CSP requires simultaneous diagonalization [23]. However, in the case of classification for more than two classes, approaches such as one-versus-rest, divide and conquer, and joint approximate diagonalization can be used for multiclass extension [1,18,23].…”

Section: Related Workmentioning

confidence: 99%

Classification of Motor Functions from Electroencephalogram (EEG) Signals Based on an Integrated Method Comprised of Common Spatial Pattern and Wavelet Transform Framework

Yahya

Musa

Ong

et al. 2019

Sensors

In this work, an algorithm for the classification of six motor functions from an electroencephalogram (EEG) signal that combines a common spatial pattern (CSP) filter and a continuous wavelet transform (CWT), is investigated. The EEG data comprise six grasp-and-lift events, which are used to investigate the potential of using EEG as input signals with brain computer interface devices for controlling prosthetic devices for upper limb movement. Selected EEG channels are the ones located over the motor cortex, C3, Cz and C4, as well as at the parietal region, P3, Pz and P4. In general, the proposed algorithm includes three main stages, band pass filtering, CSP filtering, and wavelet transform and training on GoogLeNet for feature extraction, feature learning and classification. The band pass filtering is performed to select the EEG signal in the band of 7 Hz to 30 Hz while eliminating artifacts related to eye blink, heartbeat and muscle movement. The CSP filtering is applied on two-class EEG signals that will result in maximizing the power difference between the two-class dataset. Since CSP is mathematically developed for two-class events, the extension to the multiclass paradigm is achieved by using the approach of one class versus all other classes. Subsequently, continuous wavelet transform is used to convert the band pass and CSP filtered signals from selected electrodes to scalograms which are then converted to images in grayscale format. The three scalograms from the motor cortex regions and the parietal region are then combined to form two sets of RGB images. Next, these RGB images become the input to GoogLeNet for classification of the motor EEG signals. The performance of the proposed classification algorithm is evaluated in terms of precision, sensitivity, specificity, accuracy with average values of 94.8%, 93.5%, 94.7%, 94.1%, respectively, and average area under the receiver operating characteristic (ROC) curve equal to 0.985. These results indicate a good performance of the proposed algorithm in classifying grasp-and-lift events from EEG signals.

Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

“…Vzard et al [24] employed common spatial pa ern (CSP) along with LDA to pre-process EEG data and obtained an accuracy of 71.59% on binary alertness states. Meisheri et al [14] exploited multi-class CSP (mCSP) combined with Self-Regulated Interval Type-2 Neuro-Fuzzy Inference System (SRIT2NFIS) classi er for four EEG-based motor imagery classes (movement imagination of le hand, right hand, both feet, and tongue) classi cation and achieved the accuracy of 54.63%, which is signi cantly lower than the accuracy of binary classi cation. Shiratori et al [23] achieved a similar accuracy of 56.7% using mCSP coupled to the random forests for a three-class EEG-based motor imagery task.…”

Section: Related Workmentioning

confidence: 99%

Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis

Zhang

Yao

Zhang

et al. 2018

An electroencephalography (EEG) based brain activity recognition is a fundamental eld of study for a number of signi cant applications such as intention prediction, appliance control, and neurological disease diagnosis in smart home and smart healthcare domains. Existing techniques mostly focus on binary brain activity recognition for a single person, which limits their deployment in wider and complex practical scenarios. erefore, multi-person and multi-class brain activity recognition has obtained popularity recently. Another challenge faced by brain activity recognition is the low recognition accuracy due to the massive noises and the low signal-to-noise ratio in EEG signals. Moreover, the feature engineering in EEG processing is time-consuming and highly relies on the expert experience. In this paper, we a empt to solve the above challenges by proposing an approach which has be er EEG interpretation ability via raw Electroencephalography (EEG) signal analysis for multi-person and multi-class brain activity recognition. Speci cally, we analyze inter-class and inter-person EEG signal characteristics, based on which to capture the discrepancy of inter-class EEG data. en, we adopt an Autoencoder layer to automatically re ne the raw EEG signals by eliminating various artifacts. We evaluate our approach on both a public and a local EEG datasets and conduct extensive experiments to explore the e ect of several factors (such as normalization methods, training data size, and Autoencoder hidden neuron size) on the recognition results. e experimental results show that our approach achieves a high accuracy comparing to competitive state-of-the-art methods, indicating its potential in promoting future research on multi-person EEG recognition.