EEG Channel Selection Research Articles

Efficient Brain Decoding Based on Adaptive EEG Channel Selection and Transformation

Electroencephalography (EEG) based brain-computer interface (BCI) has a wide range of applications in neuro-rehabilitation and motor assistance. However, brain activities, acquired from a large number of EEG channels, are highly inter-correlated or irrelevant to the brain decoding task, thus reducing the decoding efficiency and accuracy. How to adaptively select the optimal channel number depend on different trials remains a big challenge. To solve this problem, an efficient end-to-end brain decoding model named AdaEEGNet, is proposed in this study. It can reduce the computational cost by adaptively controlling the number of input channels and improve the classification accuracy by reducing over-fitting. Specifically, a lightweight policy module is designed to analyze which channel is needed for decoding current EEG trial. Due to the channel selection process is indifferentiable, we propose to use the Gumbel-Estimator to back-propagate the gradient to train the whole framework. Additionally, a weight coefficient is designed to make a trade-off between brain decoding accuracy and efficiency. To validate the proposed AdaEEGNet feasibility in improving decoding efficiency and accuracy, extensive experiments were conducted on BCI competition IV dataset. The results show that our methods can improve the decoding accuracy by 2% with only 65% computational cost significantly compared with the baseline method.

EEG Channel Selection Techniques in Motor Imagery Applications: A Review and New Perspectives.

Communication, neuro-prosthetics, and environmental control are just a few applications for disabled persons who use robots and manipulators that use brain-computer interface (BCI) systems. The brain's motor imagery (MI) signal is an essential input for a brain-related task in BCI applications. Due to their noninvasive, portability, and cost-effectiveness, electroencephalography (EEG) signals are the most widely used input in BCI systems. The EEG data are often collected from more than 100 different locations in the brain; channel selection techniques are critical for selecting the optimum channels for a given application. However, when analyzing EEG data, the principal purpose of channel selection is to reduce computational complexity, improve classification accuracy by avoiding overfitting, and reduce setup time. Several channel selection assessment algorithms, both with and without classification-based methods, extracted appropriate channel subsets using defined criteria. Therefore, based on the exhaustive analysis of the EEG channel selection, this manuscript analyses several existing studies to reduce the number of noisy channels and improve system performance. We review several existing works to find the most promising MI-based EEG channel selection algorithms and associated classification methodologies on various datasets. Moreover, we focus on channel selection methods that choose fewer channels with great precision. Finally, our main finding is that a smaller channel set, typically 10-30% of total channels, provided excellent performance compared to other existing studies.

An "Instantaneous" Response of a Human Visual System to Hue: An EEG-Based Study.

(1) The article presents a new technique to interpret biomedical data (EEG) to assess cortical responses to continuous color/hue variations. We propose an alternative approach to analyze EEG activity evoked by visual stimulation. This approach may augment the traditional VEP analysis. (2) Considering ensembles of EEG epochs as multidimensional spatial vectors evolving over time (rather than collections of time-domain signals) and evaluating the similarity between such vectors across different EEG epochs may result in a more accurate detection of colors that evoke greater responses of the visual system. To demonstrate its suitability, the developed analysis technique was applied to the EEG data that we previously collected from 19 participants with normal color vision, while exposing them to stimuli of continuously varying hue. (3) Orange/yellow and dark blue/violet colors generally aroused better-pronounced cortical responses. The selection of EEG channels allowed for assessing the activity that predominantly originates from specific cortical regions. With such channel selection, the strongest response to the hue was observed from Parieto-Temporal region of the right hemisphere. The statistical test-Kruskal-Wallis one-way analysis of variance-indicates that the distance evaluated for spatial EEG vectors at different post-stimulus latencies generally originate from different statistical distributions with a probability exceeding 99.9% (α = 0.001).

EEG channel selection based on sequential backward floating search for motor imagery classification.

Brain-computer interfaces (BCIs) based on motor imagery (MI) utilizing multi-channel electroencephalogram (EEG) data are commonly used to improve motor function of people with motor disabilities. EEG channel selection can enhance MI classification accuracy by selecting informative channels, accordingly reducing redundant information. The sequential backward floating search (SBFS) approach has been considered as one of the best feature selection methods. In this paper, SBFS is first implemented to select the optimal EEG channels in MI-BCI. Further, to reduce the time complexity of SBFS, the modified SBFS is proposed and applied to left and right hand MI tasks. In the modified SBFS, based on the map of EEG channels at the scalp, the symmetrical channels are selected as channel pairs and acceleration is thus realized by removing or adding multiple channels in each iteration. Extensive experiments were conducted on four public BCI datasets. Experimental results show that the SBFS achieves significantly higher classification accuracy (p < 0.001) than using all channels and conventional MI channels (i.e., C3, C4, and Cz). Moreover, the proposed method outperforms the state-of-the-art selection methods.

A multi-classification algorithm based on multi-domain information fusion for motor imagery BCI

The current problem of motor imagery Electroencephalogram(EEG) signal classification is low classification accuracy and fixed EEG channel selection. We proposed a novel classification algorithm for motor imagery EEG signals, which overcomes the contradiction between the number of channels and the representational ability of features. Higher classification accuracy is achieved using less number of channels. The algorithm makes a combination of time windows, filter banks, and an optimal sorting of the projection space to reveal multi-domain information. Experiments based on the two datasets of BCI Competition have proved that the channel selection strategy used in this paper can adapt to the subject’s neural information and select the optimal channel combination. The feature extraction algorithm proposed can achieve excellent classification accuracy (77.7 %) and kappa value (0.70). The results are improved by 26.2 % compared to the One Versus One-Common Spatial Pattern (OVO-CSP) method and by 8.2 % compared to the One Versus One-Filter bank common spatial pattern (OVO-FBCSP) method. Additionally, the proposed method has outperformed to the other state-of-the-art methods using the same data set in terms of the performance. The proposed methodology can be employed as a promising tool for a motor imagery BCI device.

EEG Signal Classification of Motor Imagery Right and Left Hand using Common Spatial Pattern and Multilayer Perceptron Back Propagation

The number of people with disabilities is increasing, so it requires bionic devices to replace human motor functions. Brain-Computer Interface (BCI) can be a tool for the bionic device to communicate with the brain. Signal brain or Electroencephalogram (EEG) signal need to classify to drive the corresponding bionic device. This research goal is to classify the imagination of the right and left-hand movements based on the EEG signal. The system design in this research consists of EEG channel selection using Finite Impulse Response (FIR) filter, feature extraction using Common Spatial Pattern (CSP), and classification using Multilayer Perceptron Back Propagation (MLP-BP). The data used a secondary dataset from BCI Competition IV (2b) with 9 research subjects. The research scenario is carried out by trying to use several variations in the number of hidden layer nodes on each EEG channel. Based on the test, the best accuracy for MLP-BP is 68.7% using 24 nodes in the alpha channel.

Motor-Imagery-Based Brain-Computer Interface Using Signal Derivation and Aggregation Functions.

Brain-computer interface (BCI) technologies are popular methods of communication between the human brain and external devices. One of the most popular approaches to BCI is motor imagery (MI). In BCI applications, the electroencephalography (EEG) is a very popular measurement for brain dynamics because of its noninvasive nature. Although there is a high interest in the BCI topic, the performance of existing systems is still far from ideal, due to the difficulty of performing pattern recognition tasks in EEG signals. This difficulty lies in the selection of the correct EEG channels, the signal-to-noise ratio of these signals, and how to discern the redundant information among them. BCI systems are composed of a wide range of components that perform signal preprocessing, feature extraction, and decision making. In this article, we define a new BCI framework, called enhanced fusion framework, where we propose three different ideas to improve the existing MI-based BCI frameworks. First, we include an additional preprocessing step of the signal: a differentiation of the EEG signal that makes it time invariant. Second, we add an additional frequency band as a feature for the system: the sensorimotor rhythm band, and we show its effect on the performance of the system. Finally, we make a profound study of how to make the final decision in the system. We propose the usage of both up to six types of different classifiers and a wide range of aggregation functions (including classical aggregations, Choquet and Sugeno integrals, and their extensions and overlap functions) to fuse the information given by the considered classifiers. We have tested this new system on a dataset of 20 volunteers performing MI-based brain-computer interface experiments. On this dataset, the new system achieved 88.80% accuracy. We also propose an optimized version of our system that is able to obtain up to 90.76%. Furthermore, we find that the pair Choquet/Sugeno integrals and overlap functions are the ones providing the best results.

Defining Common Inter-Session and Inter-Subject EEG Channels Using Spatial Selection Method

Redundancy of information on brain signals can lead to reduce brain-computer interface (BCI) performance in applications. To overcome this, EEG channel selection is performed to reduce and/or eliminate a number of channels with irrelevant information. In the previous studies, there is energy calculation methods that have been proposed to perform EEG channel selection to improve BCI performance in classifying the brain command of motor imagery stimulation. In this study, channel selection scheme on motor movement signal will be experimented by using spatial selection method. This study performs the common active channel mechanism that divided into two parts: 1) common active channels between sessions, which known as common Inter-session channels and common active channels. These two techniques can be used by all subjects to interpret motor movement type known as common Inter-subject channels. In order to validate the performance of the proposed framework, CSP (common spatial pattern) is used as a feature extraction method and k-NN with k = 3 as the classification method. The obtained results shows that the proposed channel selection technique is able to choose common active channels in five combination numbers on Inter-sessions and Inter-subjects of the acquired EEG signals. Both types of common active channels are proven to improve BCI performance with an accuracy increase of up to 66%.

Personalized attention-based EEG channel selection for epileptic seizure prediction

Epilepsy is a neurological disorder, characterized by intractable seizures with severe consequences. To predict these seizures, electroencephalogram (EEG) data has to be collected in a continuous manner. EEG signals are recorded through several electrodes fixed on the scalp that cannot be worn by a patient in a continuous manner. This paper presents a novel patient-specific approach to identify and select the most pertinent EEG channels (i.e., electrodes) without prior expert knowledge. The approach is based on a cascading deep learning model consisting of convolution blocks and attention layers. The data collected by the selected EEG channels is processed by a novel deep learning model consisting of a convolutional neural network and a gated recurrent neural network to predict epileptic seizures. Our work supports EEG sensors with a reduced number of electrodes that could be easily worn by patients for longer periods of time. The proposed approach is validated using the CHB-MIT benchmark. The obtained results show that our channel selection model outperforms existing approaches (e.g., Random Forest) by selecting fewer EEG channels. Thus, it is possible to use only two or three EEG channels for effective patient-specific epileptic seizure prediction. Experimental results also demonstrate that our selection approach reduces the prediction time.

Minimal EEG channel selection for depression detection with connectivity features during sleep

Background and objectivesSleeping cortical electroencephalogram (EEG) has the potential for depression detection, for different sleep structure and cortical connection have been proved in depressed patients. However, the operation of multi-channel sleep EEG recording is cumbersome and requires laboratory equipment and professional sleep technician. Here, we focus on the depression detection using minimal sleep EEG channels. MethodsSixteen channels of EEG data of 30 patients with depression and 30 age-matched normal controls were recorded during sleep. Power spectral density of each single EEG channel was calculated, followed by measuring the symbolic transfer entropy (STE) and weighed phase lag index (WPLI) between EEG channel pairs in various frequency bands. Thereafter, these features were evaluated by F-score in the two-way classification (depression vs. control) of 30-s sleep EEG segments. Based on the F-score, entropy method was introduced to calculate the weight which could further assess the classification ability of various EEG channels or channel pairs. Finally, machine learning was implemented to verify the important EEG channels or channel pairs in depression diagnosis. ResultsThe features characterizing the inter-hemispheric connectivity in the posterior lobe, especially in the temporal lobe, showed high classification capacity. The classification accuracy of using two and four EEG channels in the temporal lobe were 97.96% and 99.61%, respectively. ConclusionsThis study showed the possibility of using only a few sleep EEG channels for depression screening, which may greatly facilitate the diagnosis of depression outside the hospital.

Unsupervised EEG channel selection based on nonnegative matrix factorization

High-density Electroencephalogram (EEG) systems have proven to be useful in enhancing the performance of emotion recognition algorithms. However, the high-dimensional nature of this data modality may also result in irrelevant information being captured, causing overfitting problems and increasing the computational cost of downstream algorithms. To perform efficient and accurate emotion recognition, an unsupervised channel selection framework based on semi-nonnegative matrix factorization (semi-NMF) is proposed. The algorithm excels in analyzing signals with complex internal correlations and produces results that are easy to interpret. Semi-NMF was used to decompose the high-density EEG signal matrices into several activation patterns. The strongest activation pattern was considered as most related to emotion recognition and channels with large weights in that activation pattern were selected for valence-based emotion recognition. It was found that the proposed framework can effectively detect brain regions that were active during emotional activities, and, using only this reduced set of channels, achieve better recognition performance than using all channels. Compared to existing methods, the framework selects channels in a physiologically explainable way and requires no supervised feature engineering or class labels. It results in higher accuracy compared to other unsupervised energy-based methods, and on par with the supervised ReliefF method. In all, the proposed framework not only serves as a valid channel selection tool for practical emotion recognition, but also has the possibility to be transferred to other non-classification tasks, potentially contributing to a variety of EEG applications, such as brain state monitoring, pathological brain activation analysis and brain disease diagnosis.

EEG Channel Selection Based User Identification via Improved Flower Pollination Algorithm

The electroencephalogram (EEG) introduced a massive potential for user identification. Several studies have shown that EEG provides unique features in addition to typical strength for spoofing attacks. EEG provides a graphic recording of the brain’s electrical activity that electrodes can capture on the scalp at different places. However, selecting which electrodes should be used is a challenging task. Such a subject is formulated as an electrode selection task that is tackled by optimization methods. In this work, a new approach to select the most representative electrodes is introduced. The proposed algorithm is a hybrid version of the Flower Pollination Algorithm and -Hill Climbing optimizer called FPA-hc. The performance of the FPA-hc algorithm is evaluated using a standard EEG motor imagery dataset. The experimental results show that the FPA-hc can utilize less than half of the electrode numbers, achieving more accurate results than seven other methods.

Two-dimensional CNN-based distinction of human emotions from EEG channels selected by multi-objective evolutionary algorithm

In this study we explore how different levels of emotional intensity (Arousal) and pleasantness (Valence) are reflected in electroencephalographic (EEG) signals. We performed the experiments on EEG data of 32 subjects from the DEAP public dataset, where the subjects were stimulated using 60-s videos to elicitate different levels of Arousal/Valence and then self-reported the rating from 1 to 9 using the self-assessment Manikin (SAM). The EEG data was pre-processed and used as input to a convolutional neural network (CNN). First, the 32 EEG channels were used to compute the maximum accuracy level obtainable for each subject as well as for creating a single model using data from all the subjects. The experiment was repeated using one channel at a time, to see if specific channels contain more information to discriminate between low vs high arousal/valence. The results indicate than using one channel the accuracy is lower compared to using all the 32 channels. An optimization process for EEG channel selection is then designed with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with the objective to obtain optimal channel combinations with high accuracy recognition. The genetic algorithm evaluates all possible combinations using a chromosome representation for all the 32 channels, and the EEG data from each chromosome in the different populations are tested iteratively solving two unconstrained objectives; to maximize classification accuracy and to reduce the number of required EEG channels for the classification process. Best combinations obtained from a Pareto-front suggests that as few as 8–10 channels can fulfill this condition and provide the basis for a lighter design of EEG systems for emotion recognition. In the best case, the results show accuracies of up to 1.00 for low vs high arousal using eight EEG channels, and 1.00 for low vs high valence using only two EEG channels. These results are encouraging for research and healthcare applications that will require automatic emotion recognition with wearable EEG.

A novel EEG channel selection and classification methodology for multi‐class motor imagery‐based BCI system design

AbstractMulti‐class MI EEG analysis is an extensively used paradigm in BCI. However, multiple EEG channels lead to redundant information extraction and would reduce the distinction among various MI tasks. Therefore, optimal channel selection from multi‐channel EEG activity still remains a challenging task. In this study, to enhance the multi‐class BCI system's performance, a novel channel selection, and features optimization methodology have been proposed. First, multi‐channel EEG dataset is reduced to an optimum no. of channels subset using developed MDA‐SOGWO based EEG channel selection criterion. After that, the discriminable feature‐set is generated from time, frequency, and FAWT based time‐frequency domain of EEG dataset. Then, an informative feature‐set is constructed from extracted features through CCA‐RFE based feature selection criterion. Finally, training and validation of selected feature‐set are carried out using ELM, LDA, RF, and MLP classifiers. The proposed methodology is evaluated on multi‐class MI datasets of BCI Competition IV 2a and BCI Competition III 3a, yielding classification accuracy of 85.65% and 95.15%, respectively. The classification results pose the proposed methodology as an efficient method for future BCI system design.

EEG Channel Selection Using Multiobjective Cuckoo Search for Person Identification as Protection System in Healthcare Applications.

Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. Several studies defined the EEG with unique features, universality, and natural robustness to be used as a new track to prevent spoofing attacks. The EEG signals are a visual recording of the brain's electrical activities, measured by placing electrodes (channels) in various scalp positions. However, traditional EEG-based systems lead to high complexity with many channels, and some channels have critical information for the identification system while others do not. Several studies have proposed a single objective to address the EEG channel for person identification. Unfortunately, these studies only focused on increasing the accuracy rate without balancing the accuracy and the total number of selected EEG channels. The novelty of this paper is to propose a multiobjective binary version of the cuckoo search algorithm (MOBCS-KNN) to find optimal EEG channel selections for person identification. The proposed method (MOBCS-KNN) used a weighted sum technique to implement a multiobjective approach. In addition, a KNN classifier for EEG-based biometric person identification is used. It is worth mentioning that this is the initial investigation of using a multiobjective technique with EEG channel selection problem. A standard EEG motor imagery dataset is used to evaluate the performance of the MOBCS-KNN. The experiments show that the MOBCS-KNN obtained accuracy of 93.86% using only 24 sensors with AR20 autoregressive coefficients. Another critical point is that the MOBCS-KNN finds channels not too close to each other to capture relevant information from all over the head. In conclusion, the MOBCS-KNN algorithm achieves the best results compared with metaheuristic algorithms. Finally, the recommended approach can draw future directions to be applied to different research areas.

EEG Channel Selection for Epileptic Seizure Prediction Based on Minimum Variance Method

EEG Channel Selection for Epileptic Seizure Prediction Based on Minimum Variance Method

A channel independent generalized seizure detection method for pediatric epileptic seizures

Background and objectiveEpilepsy the disorder of the central nervous system has its worldwide presence in roughly 50 million people as estimated by the World Health Organization. Electroencephalogram (EEG) is one of the most common and non-invasive ways of analyzing and studying the subtle changes in neuronal activity of the brain during an epileptic seizure attack. These changes can be analyzed for developing an automated system that would assert the chances of an impending seizure. As changeable nature of seizure affects the patients from having a normal life, hence progress in developing new methods will improve the quality of life and also provide assistance in the medical sector. Objective of the proposed method is to avoid EEG channel selection and use all input EEG channel features to design a generalized epileptic seizure detection framework. MethodIn this work, a long short-term memory network has been proposed that is not complex and has the capability of effectively detecting epileptic seizures from both non-invasive and invasive electroencephalogram recordings. The proposed framework is simple and effective and designed in such capacity that raw electroencephalogram signals can be used to detect seizures. Also, a generalized approach has been followed that is channel independent such that EEG signals belonging to any hemisphere of the brain can be detected effectively by the proposed architecture. ResultsThe automated seizure detection system achieved high seizure detection sensitivity of 99.9%, and a low false-positive rate of 0.003 per hour for the Children's Hospital Boston-Massachusetts Institute of Technology dataset. While for the Sleep-Wake-Epilepsy-Center of the University Department of Neurology at the Inselspital Bern dataset, the sensitivity is 99.4% and false-positive rate of 0.006 per hour. Convergence analysis of the proposed model provides a significant amount of reliability and correctness in the efficient detection of epileptic seizures. ConclusionAssessment of the proposed framework on non-invasive as well as invasive EEG signals showed that the framework worked well for different type of EEG recordings as different metrics gave satisfactory results. As the framework is simple and did not require any additional parameter optimization techniques, it reduced the processing overheads without affecting the accuracy. Hence, it can be used as an efficient method for monitoring epileptic seizures.

Neuro-evolutionary approach for optimal selection of EEG channels in motor imagery based BCI application

The selection of relevant channels that produces optimal subset of electroencephalogram (EEG) features is of prime importance for i) reducing computational complexity, ii) reducing overfitting, and iii) eliminating inconveniences in users during clinical application. In the analysis of EEG signals acquired directly from the scalp, feature extraction and classification techniques play a key role in motor imagery based brain-computer interface (BCI) system. Typically, the high-density EEG system used in this process is composed of 64 or more channels for EEG signal recordings. However, in practical applications, fewer channels are required for the system set up. Thus, for the current study, we aim to determine the optimal number of channels for the BCI system. We propose a channel selection strategy known as neuro-evolutionary algorithm, which is based on modified particle swarm optimization (MPSO), coupled with feature extraction by common spatial pattern. To validate the proposed method, we performed experiments using 64 channel EEG recordings from four trans-humeral amputees. The acquired signals from five motor imagery tasks were fed into a wrapper based neuro-evolutionary framework after the extraction stage. The performance results of the MPSO, when compared with other models like ant colony, genetic algorithm, particle swarm optimization and simulated annealing, demonstrate the superiority of MPSO as the best performance with the most significant results. Further validation with electrocorticogram (ECoG) dataset IV of the BCI competition showed that the proposed model outperformed other commonly used methods by significantly decreasing the error rate and the number of EEG channels.

Motor imagery recognition with automatic EEG channel selection and deep learning

Objective. Modern motor imagery (MI)-based brain computer interface systems often entail a large number of electroencephalogram (EEG) recording channels. However, irrelevant or highly correlated channels would diminish the discriminatory ability, thus reducing the control capability of external devices. How to optimally select channels and extract associated features remains a big challenge. This study aims to propose and validate a deep learning-based approach to automatically recognize two different MI states by selecting the relevant EEG channels. Approach. In this work, we make use of a sparse squeeze-and-excitation module to extract the weights of EEG channels based on their contribution to MI classification, by which an automatic channel selection (ACS) strategy is developed. Further, we propose a convolutional neural network to fully exploit the time-frequency features, thus outperforming traditional classification methods both in terms of accuracy and robustness. Main results. We execute the experiments using EEG signals recorded at MI where 25 healthy subjects performed MI movements of the right hand and feet to generate motor commands. An average accuracy of is obtained, providing a 37.3% improvement with respect to a state-of-the-art channel selection approach. Significance. The proposed ACS method has been found to be significantly advantageous compared to the typical approach of using a fixed channel configuration. This work shows that fewer EEG channels not only reduces computational complexity but also improves the MI classification performance. The proposed method selects EEG channels related to hand and feet movement, which paves the way to real-time and more natural interfaces between the patient and the robotic device.

Unsupervised EEG Channel Selection Based on Nonnegative Matrix Factorization

Unsupervised EEG Channel Selection Based on Nonnegative Matrix Factorization