Automatic Channel Selection Research Articles

Automatic channel selection using shuffled frog leaping algorithm for EEG based addiction detection

Automatic channel selection using shuffled frog leaping algorithm for EEG based addiction detection

An automatic channel selection method based on the standard deviation of wavelet coefficients for motor imagery based brain–computer interfacing

AbstractThe redundant data in multichannel electroencephalogram (EEG) signals significantly reduces the performance of brain–computer interface (BCI) systems. By removing redundant channels, a channel selection strategy increases the classification accuracy of BCI systems. In this work, a novel channel selection method (stdWC) based on the standard deviation of wavelet coefficients across channels is proposed to identify Motor Imagery (MI) based EEG signals. The wavelet coefficients are calculated by employing a Continuous Wavelet Transform (CWT) filter bank to decompose each trial from the EEG channel. The wavelet coefficient's standard deviation values are obtained across the channels, and these values are then sorted to determine the EEG channels with the highest standard deviation values. The channels with the largest wavelet coefficient divergence are chosen. MI trials are then spatially filtered with the Common Spatial Pattern (CSP), and CWT filter bank‐based 2D images are generated from the spatially filtered trials. These images are then classified using a unique nine‐layered convolutional neural network (CNN) model that combines two feature maps acquired with differing filter sizes. The proposed framework (stdWC‐CSP‐CNN) is evaluated using kappa score and classification accuracy on two publically accessible datasets (BCI Competition III dataset IVa and BCI Competition IV dataset 2a). The suggested framework achieved a mean test classification accuracy of 88.8% for dataset IVa from BCI Competition III and 75.03% for dataset 2a from BCI Competition IV, according to the results. The proposed channel selection method outperforms the other channel selection methods examined, according to the results. By rejecting redundant channels, the whole framework can improve the performance of MI‐based BCIs.

An Assessment of Waveform Processing for a Single-Beam Bathymetric LiDAR System (SBLS-1).

The single-beam bathymetric light detection and ranging (LiDAR) system 1 (SBLS-1), which is equipped with a 532-nm-band laser projector and two concentric-circle receivers for shallow- and deep-water echo signals, is a lightweight and convenient prototype instrument with low energy consumption. In this study, a novel LiDAR bathymetric method is utilized to achieve single-beam and dual-channel bathymetric characteristics, and an adaptive extraction method is proposed based on the cumulative standard deviation of the peak and trough, which is mainly used to extract the signal segment and eliminate system and random noise. To adapt the dual-channel bathymetric mechanism, an automatic channel-selection method was used at various water depths. A minimum half-wavelength Gaussian iterative decomposition is proposed to improve the detection accuracy of the surface- and bottom-water waveform components and ensure bathymetric accuracy and reliability. Based on a comparison between the experimental results and in situ data, it was found that the SBLS-1 obtained a bathymetric accuracy and RMSE of 0.27 m and 0.23 m at the Weifang and Qingdao test fields. This indicates that the SBLS-1 was bathymetrically capable of acquiring a reliable, high-efficiency waveform dataset. Hence, the novel LiDAR bathymetric method can effectively achieve high-accuracy near-shore bathymetry.

Effective electroencephalogram based epileptic seizure detection using support vector machine and statistical moment’s features

Epilepsy is one of the widespread disorders. It is a noncommunicable disease that affects the human nerve system. Seizures are abnormal patterns of behavior in the electricity of the brain which produce symptoms like losing consciousness, attention or convulsions in the whole body. This paper demonstrates an effective electroencephalogram (EEG) based seizure detection method using discrete wavelet transformation (DWT) for signal decomposition to extract features. An automatic channel selection method was proposed by the researcher to select the best channel from 23 channels based on maximum variance value. The records were segmented into a nonoverlapping segment with long 1-S. The support vector machine (SVM) model was used to automatically detect segments that contain seizures, using both frequency and time domain statistical moment features. The experimental result was obtained from 24 patients in CHB-MIT database. The average accuracy is 94.1, sensitivity is 93.5, specificity is 94.6 and the false positive rate average is 0.054.

Surface EMG-based quantification of inspiratory effort: a quantitative comparison with Pes

BackgroundInspiratory patient effort under assisted mechanical ventilation is an important quantity for assessing patient–ventilator interaction and recognizing over and under assistance. An established clinical standard is respiratory muscle pressure textit{P}_{mathrm{mus}}, derived from esophageal pressure (textit{P}_{mathrm{es}}), which requires the correct placement and calibration of an esophageal balloon catheter. Surface electromyography (sEMG) of the respiratory muscles represents a promising and straightforward alternative technique, enabling non-invasive monitoring of patient activity.MethodsA prospective observational study was conducted with patients under assisted mechanical ventilation, who were scheduled for elective bronchoscopy. Airway flow and pressure, esophageal/gastric pressures and sEMG of the diaphragm and intercostal muscles were recorded at four levels of pressure support ventilation. Patient efforts were quantified via the textit{P}_{mathrm{mus}}-time product ({mathrm{PTP}}_{mathrm{mus}}), the transdiaphragmatic pressure-time product ({mathrm{PTP}}_{mathrm{di}}) and the EMG-time products (ETP) of the two sEMG channels. To improve the signal-to-noise ratio, a method for automatically selecting the more informative of the sEMG channels was investigated. Correlation between ETP and {mathrm{PTP}}_{mathrm{mus}} was assessed by determining a neuromechanical conversion factor textit{K}_{mathrm{EMG}} between the two quantities. Moreover, it was investigated whether this scalar can be reliably determined from airway pressure during occlusion maneuvers, thus allowing to quantify inspiratory effort based solely on sEMG measurements.ResultsIn total, 62 patients with heterogeneous pulmonary diseases were enrolled in the study, 43 of which were included in the data analysis. The ETP of the two sEMG channels was well correlated with {mathrm{PTP}}_{mathrm{mus}} (textit{r}={0.79pm 0.25} and textit{r}={0.84pm 0.16} for diaphragm and intercostal recordings, respectively). The proposed automatic channel selection method improved correlation with {mathrm{PTP}}_{mathrm{mus}} (textit{r}={0.87pm 0.09}). The neuromechanical conversion factor obtained by fitting ETP to {mathrm{PTP}}_{mathrm{mus}} varied widely between patients (textit{K}_{mathrm{EMG}}= {4.32pm 3.73},{hbox {cm}hbox {H}_{2}hbox {O}/upmu hbox {V}}) and was highly correlated with the scalar determined during occlusions (textit{r}={0.95}, textit{p}<{.001}). The occlusion-based method for deriving {mathrm{PTP}}_{mathrm{mus}} from ETP showed a breath-wise deviation to {mathrm{PTP}}_{mathrm{mus}} of {0.43pm 1.73},{hbox {cm}hbox {H}_{2}hbox {O},hbox {s}} across all datasets.ConclusionThese results support the use of surface electromyography as a non-invasive alternative for monitoring breath-by-breath inspiratory effort of patients under assisted mechanical ventilation.

Electrical Analysis Method for Gamma-Ray Imaging System Based on Resistive Network Readout

We present an electrical analysis method for a gamma-ray imaging system based on a resistive network readout. Currently, the array numbers of scintillators and detectors in imaging systems are being increased to enhance the spatial resolution, and resistive network-based readout circuits are used to reduce the number of channels. However, a positioning process is required to identify the channel in which the signal has been generated, which causes positional distortion. For position correction, the positioning characteristics must be measured according to the readout circuit conditions. However, due to the scintillator’s radiation, measurement in a single channel is difficult. In this work, a current pulse is modeled to analyze the positional characteristics of different channels. A current pulse generator and an automatic channel-selection circuit are designed to input signals selectively to each channel. When the input signal duration is 1 $\mu \text{s}$ , the maximum amount of charge generated by the current pulse generator is approximately 1000 pC. Position errors of up to 88.47% are generated, depending on the gain of the preamplifier. Our proposed method can be used for the linearity analysis of preamplifiers and image position correction algorithms.

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.

Epileptic Seizures Detection Using DCT-II and KNN Classifier in Long-Term EEG Signals

Epilepsy is one of the most common diseases of the nervous system around the world, affecting all age groups and causing seizures leading to loss of control for a period of time. This study presents a seizure detection algorithm that uses Discrete Cosine Transformation (DCT) type II to transform the signal into frequency-domain and extracts energy features from 16 sub-bands. Also, an automatic channel selection method is proposed to select the best subset among 23 channels based on the maximum variance. Data are segmented into frames of one Second length without overlapping between successive frames. K-Nearest Neighbour (KNN) model is used to detect those frames either to ictal (seizure) or interictal (non-seizure) based on Euclidean distance. The experimental results are tested on 21 patients included in the CHB-MIT dataset. The average F1-score was found to be 93.12, whereas the False-Positive Rate (FPR) average was determined to be 0.07.

Homography Transformation Correction Method for Position Error Generated in Readout Circuit Based on Resistive Network for the Compton Imaging System

A homography method to correct position errors generated in the Compton imaging system using a resistive network is presented. The Compton imaging system is composed of a scatterer and an absorber in multichannel arrays for high resolution and can detect gamma rays emitted from radioisotopes. Resistive networks are often used in this system to efficiently reduce the number of channels. However, this can cause position errors, and the spatial resolution deteriorates according to the resistance value of the network, type of detector array, and characteristics of the preamplifier used. Therefore, before tracking the position of the source, it is necessary to correct the position errors of images obtained from the scatterer and absorber. Also, a new correction method should consider the characteristics of the readout circuits based on the resistive network. In this work, the position errors are corrected using homography, which is a coordinate transformation method. To verify the corrections using homography transformation, we modeled the current pulse generated from the detector and designed an automatic channel selection circuit to input each channel of the resistive network. From experiments, we first obtained the positions with distortions according to the setup of readout circuits and corrected these errors by applying the homography transformation method. Consequently, the distortions were significantly corrected, and the error rates of the positions compared with those of the ideal grid were greatly reduced by up to 0.36%.

Information security of an airline computer network

In this paper workflow of the airline's computer network is reviewed. The major security threat of airline network is the usage of random local networks around the world. This threat is especially high for worldwide operating charter companies with random routes. Aircraft in remote airports are using local networks which information security level does not meet company policy. In order to increase information security of the network usage of a Softswitch was proposed. The Softswitch is located onboard of the aircraft and connected to multiple communication devices. Each time connection is made proper connection line is selected using analytic hierarchy process. Selection is based on the reliability, information security, average speed and data transfer costs criteria for each connection. Based on the criteria numerical priority value for each channel is calculated, and the best channel is selected. This approach ensures that a channel that does not meet minimal information security criteria will not be used. Based on automatic channel selection, overall information security of the network is expected. Another problem of such approach is evaluation of reliability of Softswitch control software. Software reliability can be assessed using the theory of technical systems testing, which involves the use of the sensitivity functions. However, in general case mathematical algorithms used in Softswitches generally have a nonlinear, discontinuous, and multidimensional appearance, which makes it impossible to obtain the sensitivity functions analytically. In this paper we consider application of harmonic analysis methods to obtain the sensitivity functions in an analytical form. The use of sensitivity functions allows you to identify the parameters that significantly affect the original function, and to take appropriate measures to improve the reliability of the system.

Data-driven spatial filtering for improved measurement of cortical tracking of multiple representations of speech

Objective. Measurement of the cortical tracking of continuous speech from electroencephalography (EEG) recordings using a forward model is an important tool in auditory neuroscience. Usually the stimulus is represented by its temporal envelope. Recently, the phonetic representation of speech was successfully introduced in English. We aim to show that the EEG prediction from phoneme-related speech features is possible in Dutch. The method requires a manual channel selection based on visual inspection or prior knowledge to obtain a summary measure of cortical tracking. We evaluate a method to (1) remove non-stimulus-related activity from the EEG signals to be predicted, and (2) automatically select the channels of interest. Approach. Eighteen participants listened to a Flemish story, while their EEG was recorded. Subject-specific and grand-average temporal response functions were determined between the EEG activity in different frequency bands and several stimulus features: the envelope, spectrogram, phonemes, phonetic features or a combination. The temporal response functions were used to predict EEG from the stimulus, and the predicted was compared with the recorded EEG, yielding a measure of cortical tracking of stimulus features. A spatial filter was calculated based on the generalized eigenvalue decomposition (GEVD), and the effect on EEG prediction accuracy was determined. Main results. A model including both low- and high-level speech representations was able to better predict the brain responses to the speech than a model only including low-level features. The inclusion of a GEVD-based spatial filter in the model increased the prediction accuracy of cortical responses to each speech feature at both single-subject (270% improvement) and group-level (310%). Significance. We showed that the inclusion of acoustical and phonetic speech information and the addition of a data-driven spatial filter allow improved modelling of the relationship between the speech and its brain responses and offer an automatic channel selection.

Fetal ECG extraction from time-varying and low-rank noninvasive maternal abdominal recordings

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.

An Automatic Channel Selection Approach for ICA-Based Motor Imagery Brain Computer Interface.

Independent component analysis (ICA) is a potential spatial filtering method for the implementation of motor imagery brain-computer interface (MIBCI). However, ICA-based MIBCI (ICA-MIBCI) is sensitive to electroencephalogram (EEG) channels and the quality of the training data, which are two crucial factors affecting the stability and classification performance of ICA-MIBCI. To address these problems, this paper is mainly focused on the investigation of EEG channel optimization. As a reference, we constructed a single-trial-based ICA-MIBCI system with commonly used channels and common spatial pattern-based MIBCI (CSP-MIBCI). To minimize the impact of artifacts on EEG channel optimization, a data-quality evaluation method, named "self-testing" in this paper, was used in a single-trial-based ICA-MIBCI system to evaluate the quality of single trials in each dataset; the resulting self-testing accuracies were used for the selection of high-quality trials. Given several candidate channel configurations, ICA filters were calculated using selected high-quality trials and applied to the corresponding ICA-MIBCI implementation. Optimal channels for each dataset were assessed and selected according to the self-testing results related to various candidate configurations. Forty-eight MI datasets of six subjects were employed in this study to validate the proposed methods. Experimental results revealed that the average classification accuracy of the optimal channels yielded a relative increment of 2.8% and 8.5% during self-testing, 14.4% and 9.5% during session-to-session transfer, and 36.2% and 26.7% during subject-to-subject transfer compared to CSP-MIBCI and ICA-MIBCI with fixed the channel configuration. This work indicates that the proposed methods can efficiently improve the practical feasibility of ICA-MIBCI.

Survey of technologies of self-organizing networks (SON)

Due to increase in existing web availability, self organized systems are expected to give a quick, adaptable and cost-effective solution for satisfy the gap between limit and request/demand to adapt to the quick development in remote activity or wireless traffic. The main purpose of introducing SON algorithm is to simplify the network operations via automating repeated tasks in network operations. Hence, in SON empowered system operations, methods that hold the human administrator in manual system operations are substituted via programmed SON functions. Self organized systems or intelligent frameworks are required for remote portable systems to deal with broad number of clients in the meantime and to support the human administrator in manual system operations; that are substituted via automatic SON functions since the human administrators don't have to gather and examine the system information. Self organized networks are needed for the controlling the big challenge of mobile traffic growth and to reduce the cost efficiency. Efficient methods are required for automatic channel selection, power adjustment, and frequency assignment for autonomous interference coordination and coverage optimization.

Multichannel EEG-Based Emotion Recognition via Group Sparse Canonical Correlation Analysis

In this paper, a novel group sparse canonical correlation analysis (GSCCA) method is proposed for simultaneous electroencephalogram (EEG) channel selection and emotion recognition. GSCCA is a group sparse extension of the conventional CCA method to model the linear correlationship between emotional EEG class label vectors and the corresponding EEG feature vectors. In contrast to conventional CCA method or previous GSCCA methods, a major advantage of our GSCCA method is the ability of handling the group feature selection problem from raw EEG features, which makes it very suitable for simultaneously coping with both EEG emotion recognition and automatic channel selection issues where each EEG channel is associated with a group of raw EEG features. To deal with EEG emotion recognition problem, we adopt the popularly used frequency feature to describe the EEG signal by dividing the full EEG frequency band into five parts, i.e., $\boldsymbol {\delta }$ , $\boldsymbol {\theta }$ , $\boldsymbol {\alpha }$ , $\boldsymbol {\beta }$ , and $\boldsymbol {\gamma }$ frequency bands, and then extract the frequency band features from each band for GSCCA model learning and emotion recognition. Finally, we conduct extensive experiments on EEG-based emotion recognition based on the SJTU emotion EEG dataset and experimental results demonstrate that the proposed GSCCA method would outperform the state-of-the-art EEG-based emotion recognition approaches.

Supervised learning in automatic channel selection for epileptic seizure detection

Detecting seizure using brain neuroactivations recorded by intracranial electroencephalogram (iEEG) has been widely used for monitoring, diagnosing, and closed-loop therapy of epileptic patients, however, computational efficiency gains are needed if state-of-the-art methods are to be implemented in implanted devices. We present a novel method for automatic seizure detection based on iEEG data that outperforms current state-of-the-art seizure detection methods in terms of computational efficiency while maintaining the accuracy. The proposed algorithm incorporates an automatic channel selection (ACS) engine as a pre-processing stage to the seizure detection procedure. The ACS engine consists of supervised classifiers which aim to find iEEG channels which contribute the most to a seizure. Seizure detection stage involves feature extraction and classification. Feature extraction is performed in both frequency and time domains where spectral power and correlation between channel pairs are calculated. Random Forest is used in classification of interictal, ictal and early ictal periods of iEEG signals. Seizure detection in this paper is retrospective and patient-specific. iEEG data is accessed via Kaggle, provided by International Epilepsy Electro-physiology Portal. The dataset includes a training set of 6.5 h of interictal data and 41 min in ictal data and a test set of 9.14 h. Compared to the state-of-the-art on the same dataset, we achieve 2 times faster in run-time seizure detection. The proposed model is able to detect a seizure onset at 89.40% sensitivity and 89.24% specificity with a mean detection delay of 2.63 s for the test set. The area under the ROC curve (AUC) is 96.94%, that is comparable to the current state-of-the-art with AUC of 96.29%.

Automatic channel selection in EEG signals for classification of left or right hand movement in Brain Computer Interfaces using improved binary gravitation search algorithm

Abstract This paper presents an automatic method for finding optimal channels in Brain Computer Interfaces (BCIs). Detecting the effective channels in BCI systems is an important problem in reducing the complexity of these systems. In this research, Improved Binary Gravitation Search Algorithm (IBGSA) is used to automatically detect the effective electroencephalography (EEG) channels in left or right hand classification. To do this, at first, data is filtered with a bandpass filter in order to reduce the amount of different types of merged noise. Then, the electrooculography (EOG) and electromyography (EMG) artifacts are corrected based on Blind Source Separation (BSS) algorithm. Data is epoched according to the left or right hand motor imageries and central beta frequency band is isolated for Event Related Synchronization (ERS) analysis. Feature extraction process is carried out by analyzing EEG signals in time and wavelet domains. The logarithmic power of each channel is computed in time domain and the features of mean, mode, median, variance, and standard deviation are calculated in wavelet domain. IBGSA is employed to detect the optimal channels to achieve better classification results. Support Vector Machine (SVM) is used as the classifier. The maximum accuracy of 80% and average accuracy of 76.24% were obtained for eight subjects in BCI competition IV dataset. The results of this research confirm that automatically detecting effective channels can enhance the practical implementation of BCI based systems and reduce the complexity.

Optimizing Spike-Sorting Yield through Selection of Optimal Recording Sites in High-density Microelectrode Arrays

Optimizing Spike-Sorting Yield through Selection of Optimal Recording Sites in High-density Microelectrode Arrays

Preliminary applications of a land surface temperature retrieval method to IASI and AIRS data

Land surface temperature (LST) is one of the key state variables for many applications. This article aims to apply our previously developed LST retrieval method to infrared atmospheric sounding interferometer (IASI) and atmospheric infrared sounder (AIRS) data. On the basis of the opposite characteristics of the atmospheric spectral absorption and surface spectral emissivity, a ‘downwelling radiance residual index’ (DRRI) has been recalled and improved to obtain LST and emissivity. To construct an efficient DRRI, an automatic channel selection procedure has been proposed, and 11 groups of channels have been selected within the range 800–1000 cm−1. The DRRI has been tested with IASI and AIRS data. For the IASI data, the radiosonde data have been used to correct for atmospheric effects and to retrieve LST, while the atmospheric profiles retrieved from AIRS data have been used to perform the atmospheric corrections and subsequently to estimate LST from AIRS data. The differences between IASI- and Moderate Resolution Imaging Spectroradiometer (MODIS)-derived LSTs are no more than 2 K, while the differences between AIRS- and MODIS-derived LSTs are less than 5 K. Even though an exceptionally problematic value occurred (–12.89 K), the overall differences between AIRS-estimated LST and the AIRS L2 LST product are no more than 5 K. Although the IASI-derived LST is more accurate than the AIRS-derived one, the convenient retrieval of AIRS atmospheric profile made this method more applicable. Limitations and uncertainties in retrieving LST using the DRRI method are also discussed.

Regularized logistic regression and multiobjective variable selection for classifying MEG data

This paper addresses the question of maximizing classifier accuracy for classifying task-related mental activity from Magnetoencelophalography (MEG) data. We propose the use of different sources of information and introduce an automatic channel selection procedure. To determine an informative set of channels, our approach combines a variety of machine learning algorithms: feature subset selection methods, classifiers based on regularized logistic regression, information fusion, and multiobjective optimization based on probabilistic modeling of the search space. The experimental results show that our proposal is able to improve classification accuracy compared to approaches whose classifiers use only one type of MEG information or for which the set of channels is fixed a priori.