Canonical Analysis for Increased Classification Speed and Channel Selection

Because of the redundant information contained in the EEG signals, the classification accuracy of motor imagery may be greatly reduced. The channel selection method helps to remove task-independent EEG signals, thereby improving the performance of the BCI system. However, the brain regions associated with motor imagery in different subjects are not exactly same, and the method of channel selection depends on the feedback of classification results. Aiming at the above problems, this paper proposed a new method of channel selection based on backward elimination and threshold. First, we calculated the variance of channels between categories based on the covariance matrix. The backward selection and threshold selection methods were then used to retain the highly differentiated channels. We also used different criterion to evaluate the discriminability between different classes. The selected EEG signals used common spatial pattern (CSP) and support vector machine (SVM) to calculate the classification accuracy. The efficacy of the proposed approach was examined using three data sets. The proposed approach has achieved 77.82% accuracy on the BCI Competition IV data set IIa, 86.02% accuracy on the BCI Competition III data set IIIa, and 86.86% accuracy on the binary class BCI Competition III data set IVa. The performance of the proposed algorithm is compared with other existing algorithms. The results of our experiments demonstrated that the proposed algorithm produces a higher classification accuracy compared to the other algorithms using lesser number of channels.

Background Due to the redundant information contained in multichannel electroencephalogram (EEG) signals, the classification accuracy of brain-computer interface (BCI) systems may deteriorate to a large extent. Channel selection methods can help to remove task-independent electroencephalogram (EEG) signals and hence improve the performance of BCI systems. However, in different frequency bands, brain areas associated with motor imagery are not exactly the same, which will result in the inability of traditional channel selection methods to extract effective EEG features. New Method To address the above problem, this paper proposes a novel method based on common spatial pattern- (CSP-) rank channel selection for multifrequency band EEG (CSP-R-MF). It combines the multiband signal decomposition filtering and the CSP-rank channel selection methods to select significant channels, and then linear discriminant analysis (LDA) was used to calculate the classification accuracy. Results The results showed that our proposed CSP-R-MF method could significantly improve the average classification accuracy compared with the CSP-rank channel selection method.

The electroencephalogram (EEG) signal is the key signal carrier of the brain-computer interface (BCI) system. The EEG data collected by the whole-brain electrode arrangement is conducive to obtaining higher information representation. Personalized electrode layout, while ensuring the accuracy of EEG signal decoding, can also shorten the calibration time of BCI and has become an important research direction. This paper reviews the EEG signal channel selection methods in recent years, conducts a comparative analysis of the combined effects of different channel selection methods and different classification algorithms, obtains the commonly used channel combinations in motor imagery, P300 and other paradigms in BCI, and explains the application scenarios of the channel selection method in different paradigms are discussed, in order to provide stronger support for a more accurate and portable BCI system.

A robust and subject-specific sequential forward search method for effective channel selection in brain computer interfaces

In the field of brain-computer interface (BCI) based on motor imagery (MI), multi-channel electroencephalography (EEG) data is commonly utilized for MI task recognition to achieve sensory compensation or precise human-computer interaction. However, individual physiological differences, environmental variations, or redundant information and noise in certain channels can pose challenges and impact the performance of BCI systems. In this study, we introduce a channel selection method utilizing Hybrid-Recursive Feature Elimination (H-RFE) combined with residual graph neural networks for MI recognition. This channel selection method employs a recursive feature elimination strategy and integrates three classification methods, namely random forest, gradient boosting, and logistic regression, as evaluators for adaptive channel selection tailored to specific subjects. To fully exploit the spatiotemporal information of multi-channel EEG, this study employed a graph neural network embedded with residual blocks to achieve precise recognition of motor imagery. We conducted algorithm testing using the SHU dataset and the PhysioNet dataset. Experimental results show that on the SHU dataset, utilizing 73.44% of the total channels, the cross-session MI recognition accuracy is 90.03%. Similarly, in the PhysioNet dataset, using 72.5% of the channel data, the classification result also reaches 93.99%. Compared to traditional strategies such as selecting three specific channels, correlation-based channel selection, mutual information-based channel selection, and adaptive channel selection based on Pearson coefficients and spatial positions, the proposed method improved classification accuracy by 34.64%, 10.8%, 3.25% and 2.88% on the SHU dataset, and by 46.96%, 5.04%, 5.81% and 2.32% on the PhysioNet dataset, respectively.

This paper presents an automatic selected channel method for improving brain-computer interface (BCI) performance. Finding an effective channel composition in a BCI system is important for parsing the complexity of these systems. In this study, several statistical methods based on an energy extraction method were used to automatically detect the composition of selected channels. We introduce four techniques for automatic channel selection to optimize the energy extraction method, such as mean, high mean, box, and high in box. The performance of all of the proposed techniques was evaluated based on performance accuracy, compression ratio, and channel mapping on a brain scalp. The performance of the proposed BCI framework was compared against a BCI framework that used a manual technique as the energy extraction method. The proposed BCI framework system used a conventional common spatial pattern (CSP) to extract features from two-class motor imagery EEG signals before employing extreme learning machine (ELM) to classify the features of the EEG signal. As a result, the proposed automatic channel selection methods were found effective in finding optimal channels and provided better performance accuracy. In general, the proposed method improved the conventional BCI performance by up to 16% accuracy and 87% channel compression size. Besides that, the automatic technique also yielded better BCI performance accuracy of up to 5% compared to the BCI system that used the manual technique of energy extraction as its EEG channel selection method.

Dynamic spectrum Access (DSA) is used to make use of the spectrum in wireless network. In most situations, control channel is used as a common approach to negotiate between secondary users. However, based on this feature, the whole network can be destroyed by influencing the control channel only. This paper analyzed and simulated two channel selection methods (Random channel selection and Receiver-directed channel selection) without control channel. And we design a dynamic spectrum access environment and use different topologies to simulate and analysis the algorithms. The results prove that those methods are available. The Receiver-directed channel selection is more match to the regular control channel standards in throughput by comparisons, and the attack probability of network is decreased by the successful spectrum channel handoff.

Brain computer interface (BCI) is a technology recording brain signals and translate into machine language to drive device movement. To improve the convenience and practicability of BCI systems, it is necessary to reduce the number of channels of EEG without reducing classification accuracy. In this paper, a channel selection method was proposed. Combine continuous wavelet transform (CWT) with EEG source imaging (ESI), we can find out the source distribution of two kinds of motor imagery (MI) tasks on the cortex. Then the different value of source distribution on the cortex was projected to scalp. The value on the scalp was treated as a criterion for channel selection. While ESI was applied for locating the active areas, we can not only find out the prior channels clearly, but also figure out the expected areas briefly. The satisfying results show that the combination of CWT and ESI is a potential method for dealing with MI tasks.

During electroencephalography (EEG)-based motor imagery-brain-computer interfaces (MI-BCIs) task, a large number of electrodes are commonly used, and consume much computational resources. Therefore, channel selection is crucial while ensuring classification accuracy. This paper proposes a channel selection method by integrating the efficient channel attention (ECA) module with a convolutional neural network (CNN). During model training process, the ECA module automatically assigns the channel weights by evaluating the relative importance for BCI classification accuracy of every channel. Then a ranking of EEG channel importance can be established so as to select an appropriate number of channels to form a channel subset from the ranking. In this paper, the ECA module is embedded into a commonly used network for MI, and comparative experiments are conducted on the BCI Competition IV dataset 2a. The proposed method achieved an average accuracy of 75.76% with all 22 channels and 69.52% with eight channels in a four-class classification task, outperforming other state-of-the-art EEG channel selection methods. The result demonstrates that the proposed method provides an effective channel selection approach for EEG-based MI-BCI.

The rapid serial visual presentation-based brain–computer interface (RSVP-BCI) system achieves the recognition of target images by extracting event-related potential (ERP) features from electroencephalogram (EEG) signals and then building target classification models. Currently, how to reduce the training and calibration time for classification models across different subjects is a crucial issue in the practical application of RSVP. To address this issue, a zero-calibration (ZC) method termed Attention-ProNet, which involves meta-learning with a prototype network integrating multiple attention mechanisms, was proposed in this study. In particular, multiscale attention mechanisms were used for efficient EEG feature extraction. Furthermore, a hybrid attention mechanism was introduced to enhance model generalization, and attempts were made to incorporate suitable data augmentation and channel selection methods to develop an innovative and high-performance ZC RSVP-BCI decoding model algorithm. The experimental results demonstrated that our method achieved a balance accuracy (BA) of 86.33% in the decoding task for new subjects. Moreover, appropriate channel selection and data augmentation methods further enhanced the performance of the network by affording an additional 2.3% increase in BA. The model generated by the meta-learning prototype network Attention-ProNet, which incorporates multiple attention mechanisms, allows for the efficient and accurate decoding of new subjects without the need for recalibration or retraining.

This article proposes an access-point and channel selection method for Internet of Things environments. Recently, the number of wireless nodes has increased with the growth of Internet of Things technologies. In order to accommodate traffic generated by the wireless nodes, we need to utilize densely placed wireless access-points. This article introduces a joint optimization problem of access-point and channel selection for such an environment. The proposed method deals with the optimization problem, using Markov approximation which adapts to dynamic changes in network conditions. Markov approximation is a distributed optimization framework, where a network is optimized by individual behavior of users forming a time-reversible continuous-time Markov chain. The proposed method searches optimal solution for the access-point and channel selection problem on the time-reversible continuous-time Markov chain. Simulation experiments demonstrate the effectiveness of the proposed method.

Channel selection is important for automatic speech recognition as the signal quality of one channel might be significantly better than those of the other channels and therefore, microphone array or blind source separation techniques might not lead to improvements over the best single microphone. The mayor challenge, however, is to find this particular channel who is leading to the most accurate classification. In this paper we present a novel channel selection method, based on class separability, to improve multi-source far distance speech-totext transcriptions. Class separability measures have the advantage, compared to other methods such as the signal to noise ratio (SNR), that they are able to evaluate the channel quality on the actual features of the recognition system. We have evaluated on NISTs RT-07 development set and observe significant improvements in word accuracy over SNR based channel selection methods. We have also used this technique in NISTs RT-07 evaluation.

Objective: Noninvasive fetal electrocardiography is emerging as a low-cost and high-accuracy technology for fetal cardiac monitoring. Signal processing techniques have been used over the past fifty years in this domain. The current major challenges of this domain, addressed in this study are (1) fetal electrocardiogram (fECG) extraction from few numbers of maternal abdominal channels in low signal-to-noise ratios; (2) online fECG extraction; (3) automatic and online signal quality assessment and channel selection; and (4) accurate and robust fetal R-peak detection and ECG parameter extraction. Approach: Based on the theory of cyclostationarity, auxiliary maternal ECG channel(s) are synthetically constructed and augmented with the input channels. The augmented data are used to develop a robust multichannel source separation algorithm for online/offline fECG extraction, from as few as a single channel, and an accurate fetal R-peak detector using a two-pass matched filter. Several robust signal quality indexes (SQI) and a voting strategy are also proposed for automatic fetal signal quality assessment. Main results: It is shown that the fECG and the fetal R-peaks can be accurately extracted from standard online available datasets, for which classical source separation methods (requiring many channels) had previously failed. The signal quality indexes fully automate the extraction and channel selection procedure. Finally, the proposed R-peak detector is highly robust to background noise and residual maternal R-peak components. Significance: The proposed methods for fECG extraction, R-peak detection and automatic channel selection are evaluated (visually and numerically), on two online available datasets and compared with recently developed algorithms. The proposed algorithm is statistically shown to outperform the benchmarks in terms of average and standard deviation.

The alcoholism can be detected by analyzing electroencephalogram (EEG) signals. However, analyzing multi-channel EEG signals is a challenging task, which often requires complicated calculations and long execution time. This paper proposes three data selection methods to extract representative data from the EEG signals of alcoholics. The methods are the principal component analysis based on graph entropy (PCA-GE), the channel selection based on graph entropy (GE) difference, and the mathematic combinations channel selection, respectively. For comparison purposes, the selected data from the three methods are then classified by three classifiers: the J48 decision tree, the K-nearest neighbor and the Kstar, separately. The experimental results show that the proposed methods are successful in selecting data without compromising the classification accuracy in discriminating the EEG signals from alcoholics and non-alcoholics. Among them, the proposed PCA-GE method uses only 29.69% of the whole data and 29.5% of the computation time but achieves a 94.5% classification accuracy. The channel selection method based on the GE difference also gains a 91.67% classification accuracy by using only 29.69% of the full size of the original data. Using as little data as possible without sacrificing the final classification accuracy is useful for online EEG analysis and classification application design.

Electroencephalography (EEG) stands as a noninvasive and cost-effective method for recording neural activity, holding potential for applications such as identifying neural processes underlying human emotions. This paper delves into the transferability and generalizability of EEG channel selection in emotion recognition, adopting a dataset-independent approach. By leveraging Power Spectral Density (PSD), we identify high-contributing EEG channels in the SEED V dataset and validate our approach on the independent SEED IV dataset using a Convolutional Neural Network (CNN) model. The channel selection method helped in eliminating insignificant EEG channels, which can improve the applicability of developing more efficient EEG devices for daily use to monitor emotions, as well as in individuals suffering from various neurodegenerative diseases. Through extensive experiments varying the number of channels and features, our model achieves classification accuracies of 77.02%, 75.42%, 71.31%, and 64.31% with 62, 30, 20, and 10 EEG channels, accompanied by 310, 90, 60, and 30 Differential Entropy (DE) features respectively. Further, the proposed approach is tested by introducing Gaussian noise to the training set and evaluating its sensitivity to signal noise. Finally, results are compared with state-of-the-art models highlighting the potential of our dataset-independent channel selection method.