Optimal Channel Set Research Articles

Optimal Channel Selection of Spaceborne Microwave Radiometer for Surface Pressure Retrieval over Oceans

Abstract The O2-band channel configuration of existing microwave radiometers is not optimal for surface pressure retrieval, which limits the surface pressure retrieval accuracy. In this study, we present the results of theoretically what might be the optimal microwave channels for surface pressure retrieval. An improved iterative selection method is used to select the channels that contain the highest cumulative content of surface pressure information. The selected optimal channel set comprises 16 channels, among which 10 channels are centered at the 50–60 GHz oxygen absorption band and 6 channels are centered around the 118.75 GHz oxygen absorption line. Two representative spaceborne microwave radiometers are used for comparisons, the Advanced Technology Microwave Sounder (ATMS) on board the Suomi National Polar-Orbiting Partnership (SNPP) satellite and the Microwave Humidity and Temperature Sounder (MWHTS) on board the Chinese Fengyun-3C (FY-3C) satellite. The results of information content analysis show that the optimal channel set contains more surface pressure information than that of the combination of SNPP/ATMS and FY-3C/MWHTS (SNPP/ATMS+FY-3C/MWHTS) channels. A representative dataset from the ERA5 data is input into the plane-parallel Microwave Radiative Transfer model to obtain the simulated brightness temperature observations of the selected optimal channels and the SNPP/ATMS+FY-3C/MWHTS channels. Using the simulated observations, retrieval experiments are performed. Experimental results show that retrieval accuracies of the optimal channel set are 1.09 and 1.64 hPa for clear-sky and cloudy conditions, respectively. The retrieval accuracies are 0.60 and 0.65 hPa better than that of the SNPP/ATMS+FY-3C/MWHTS channels for clear-sky and cloudy conditions, respectively.

Musical Emotions Recognition Using Entropy Features and Channel Optimization Based on EEG.

The dynamic of music is an important factor to arouse emotional experience, but current research mainly uses short-term artificial stimulus materials, which cannot effectively awaken complex emotions and reflect their dynamic brain response. In this paper, we used three long-term stimulus materials with many dynamic emotions inside: the "Waltz No. 2" containing pleasure and excitement, the "No. 14 Couplets" containing excitement, briskness, and nervousness, and the first movement of "Symphony No. 5 in C minor" containing passion, relaxation, cheerfulness, and nervousness. Approximate entropy (ApEn) and sample entropy (SampEn) were applied to extract the non-linear features of electroencephalogram (EEG) signals under long-term dynamic stimulation, and the K-Nearest Neighbor (KNN) method was used to recognize emotions. Further, a supervised feature vector dimensionality reduction method was proposed. Firstly, the optimal channel set for each subject was obtained by using a particle swarm optimization (PSO) algorithm, and then the number of times to select each channel in the optimal channel set of all subjects was counted. If the number was greater than or equal to the threshold, it was a common channel suitable for all subjects. The recognition results based on the optimal channel set demonstrated that each accuracy of two categories of emotions based on "Waltz No. 2" and three categories of emotions based on "No. 14 Couplets" was generally above 80%, respectively, and the recognition accuracy of four categories based on the first movement of "Symphony No. 5 in C minor" was about 70%. The recognition accuracy based on the common channel set was about 10% lower than that based on the optimal channel set, but not much different from that based on the whole channel set. This result suggested that the common channel could basically reflect the universal features of the whole subjects while realizing feature dimension reduction. The common channels were mainly distributed in the frontal lobe, central region, parietal lobe, occipital lobe, and temporal lobe. The channel number distributed in the frontal lobe was greater than the ones in other regions, indicating that the frontal lobe was the main emotional response region. Brain region topographic map based on the common channel set showed that there were differences in entropy intensity between different brain regions of the same emotion and the same brain region of different emotions. The number of times to select each channel in the optimal channel set of all 30 subjects showed that the principal component channels representing five brain regions were Fp1/F3 in the frontal lobe, CP5 in the central region, Pz in the parietal lobe, O2 in the occipital lobe, and T8 in the temporal lobe, respectively.

Classification of lower limb motor imagery based on iterative EEG source localization and feature fusion

Motor imagery (MI) brain–computer interface (BCI) systems have broad application prospects in rehabilitation and other fields. However, to achieve accurate and practical MI-BCI applications, there are still several critical issues, such as channel selection, electroencephalogram (EEG) feature extraction and EEG classification, needed to be better resolved. In this paper, these issues are studied for lower limb MI which is more difficult and less studied than upper limb MI. First, a novel iterative EEG source localization method is proposed for channel selection. Channels FC1, FC2, C1, C2 and Cz, instead of the commonly used traditional channel set (TCS) C3, C4 and Cz, are selected as the optimal channel set (OCS). Then, a multi-domain feature (MDF) extraction algorithm is presented to fuse single-domain features into multi-domain features. Finally, a particle swarm optimization based support vector machine (SVM) method is utilized to classify the EEG data collected by the lower limb MI experiment designed by us. The results show that the classification accuracy is 88.43%, 3.35–5.41% higher than those of using traditional SVM to classify single-domain features on the TCS, which proves that the combination of OCS and MDF can not only reduce the amount of data processing, but also retain more feature information to improve the accuracy of EEG classification.

Brain–computer interface channel selection optimization using meta-heuristics and evolutionary algorithms

Many brain–computer interface (BCI) studies overlook the channel optimization due to its inherent complexity. However, a careful channel selection increases the performance and users’ comfort while reducing the cost of the system. Evolutionary meta-heuristics, which have demonstrated their usefulness in solving complex problems, have not been fully exploited yet in this context. The purpose of the study is two-fold: (1) to propose a novel algorithm to find an optimal channel set for each user and compare it with other existing meta-heuristics; and (2) to establish guidelines for adapting these optimization strategies to this framework. A total of 3 single-objective (GA, BDE, BPSO) and 4 multi-objective (NSGA-II, BMOPSO, SPEA2, PEAIL) existing algorithms have been adapted and tested with 3 public databases: ‘BCI competition III-dataset II’, ‘Center Speller’ and ‘RSVP Speller’. Dual-Front Sorting Algorithm (DFGA), a novel multi-objective discrete method especially designed to the BCI framework, is proposed as well. Results showed that all meta-heuristics outperformed the full set and the common 8-channel set for P300-based BCIs. DFGA showed a significant improvement of accuracy of 3.9% over the latter using also 8 channels; and obtained similar accuracies using a mean of 4.66 channels. A topographic analysis also reinforced the need to customize a channel set for each user. Thus, the proposed method computes an optimal set of solutions with different number of channels, allowing the user to select the most appropriate distribution for the next BCI sessions.

Optimal Channel-set and Feature-set Assessment for Foot Movement Based EMG Pattern Recognition

ABSTRACT Electromyography (EMG) -based control is the most convenient and robust way to classify body movements for controlling prosthetic as well as orthotic devices. Its translation from lab-based approach to assistive devices demands a problem-centric and cost-effective solution. This paper demonstrates its utility for the classification of four foot movements, viz Plantar flexion, Dorsi flexion, Eversion and Inversion. For the experimental study, four superficial muscles (viz. Tibialis Anterior, Extensor Hallucis Longus, Gastrocnemius Medial and Fibularis Longus) were identified as electrode positioning locations for the EMG data acquisition. This work is aimed to minimize the number of electrode locations without significantly affecting the classification performance. Channel-set CH2,4 corresponding to the combination of Hallucis Longus and Fibularis Longus muscles is found to be the most optimal. The maximum classification accuracy obtained for the given set with the selected feature-set has been (91.85 ± 3.57)%. The classification performance has been assessed on the basis of parameters such as the type of classifier, window length, data sampling and also the body mass index of the participants. The developed technique can be applied for control of ankle exoskeletons for healthy as well as person with certain disabilities.

A portable HCI system‐oriented EEG feature extraction and channel selection for emotion recognition

Emotion recognition has become an important component of human–computer interaction systems. Research on emotion recognition based on electroencephalogram (EEG) signals are mostly conducted by the analysis of all channels' EEG signals. Although some progresses are achieved, there are still several challenges such as high dimensions, correlation between different features and feature redundancy in the realistic experimental process. These challenges have hindered the applications of emotion recognition to portable human–computer interaction systems (or devices). This paper explores how to find out the most effective EEG features and channels for emotion recognition so as to only collect data as less as possible. First, discriminative features of EEG signals from different dimensionalities are extracted for emotion classification, including the first difference, multiscale permutation entropy, Higuchi fractal dimension, and discrete wavelet transform. Second, relief algorithm and floating generalized sequential backward selection algorithm are integrated as a novel channel selection method. Then, support vector machine is employed to classify the emotions for verifying the performance of the channel selection method and extracted features. At last, experimental results demonstrate that the optimal channel set, which are mostly located at the frontal, has extremely high similarity on the self-collected data set and the public data set and the average classification accuracy is achieved up to 91.31% with the selected 10-channel EEG signals. The findings are valuable for the practical EEG-based emotion recognition systems.

Electrode channel selection based on backtracking search optimization in motor imagery brain-computer interfaces.

Common spatial pattern algorithm is widely used to estimate spatial filters in motor imagery based brain-computer interfaces. However, use of a large number of channels will make common spatial pattern tend to over-fitting and the classification of electroencephalographic signals time-consuming. To overcome these problems, it is necessary to choose an optimal subset of the whole channels to save computational time and improve the classification accuracy. In this paper, a novel method named backtracking search optimization algorithm is proposed to automatically select the optimal channel set for common spatial pattern. Each individual in the population is a N-dimensional vector, with each component representing one channel. Apopulation of binary codes generate randomly in the beginning, and then channels are selected according to the evolution of these codes. The number and positions of 1's in the code denote the number and positions of chosen channels. The objective function of backtracking search optimization algorithm is defined as the combination of classification error rate and relative number of channels. Experimental results suggest that higher classification accuracy can be achieved with much fewer channels compared to standard common spatial pattern with whole channels.

Cloud characteristics and channel selection for IASI radiances in meteorologically sensitive areas

AbstractThe cloudiness in simulated Infrared Atmospheric Sounding Interferometer (IASI) pixels deduced from the Advanced Very‐High‐Resolution Radiometer (AVHRR) satellite imager is studied specifically in meteorologically sensitive areas during the Fronts and Atlantic Storm‐Track Experiment. It is found that few clear AVHRR observations are located in the IASI pixels in these regions, which are covered by high‐level and low‐level clouds. The IASI channel selection is then studied in the context of the sensitive areas for the pixels with low‐level clouds. The Entropy Reduction (ER) method, which was previously studied in a general context, is compared with two other channel selection methods using selection criteria based on adjoint sensitivity: the sensitivity to observations and the so‐called Kalman‐filter sensitivity. It is found that, even though the ‘sensitive’ methods give slightly better results than the ER one, the latter performs quite robustly and at a lower computational cost. The robustness to the specification of the background‐error covariance matrix is then studied. It is shown that channel selection based on the ER method is particularly robust to the specification of the background‐error covariance matrix, but the analysis step itself requires an accurate determination of the background‐error covariance matrix. In addition it is shown that an independently computed constant channel set gives comparable results to the optimal channel set. Copyright © 2004 Royal Meteorological Society.

Optimal use of high resolution infrared sounder channels in atmospheric profile retrieval

Due to correlations among high resolution infrared sounder radiances and retrieval computation efficiency, it is prudent to use a set of statistically less correlated channels which not only retains the majority of measurement information but also yields the most accurate atmospheric profile retrievals. The statistical procedure of these channels selection is described in the paper. The use of optimal selected channels simulated for the measurements of AIRS (Atmospheric InfRared Sounder) in the statistical / physical retrieval is discussed. Simulated AIRS inversion results demonstrate the advantages of using optimal channel set that produce the accurate and stable retrieval solution.