Channel selection by class separability measures for automatic transcriptions on distant microphones

Elicitation of fetal ECG from abdominal recordings using Blind Source Separation techniques and Robust Set Membership Affine Projection algorithm for signal quality enhancement

This work focuses on an infant deafness detector unit, using the concept of microphone array. This instrument is based on the principle of evoked acoustic emissions (OAEs). The key feature of the microphone array is its ability to increase signal-to-noise ratio (SNR) and reproducibility of the OAE responses. These further significantly contribute to improve the sensitivity and specificity of the overall system. Low level sound pressure values are recorded by the sensitive microphones in microphone array unit and processed using TI's DSP6416. The sound stimulus transmitted to human ear is generated and controlled by the 6416 DSP (Digital signal processor). Hardware circuit details and the algorithm used in signal processing are discussed in this paper. Standard averaging technique is used in the implemented algorithm. The final result speaks about the hearing capacity of a patient. The proof that the usage of microphone arrays leads to better SNR values than using a single microphone in an OAE probe, is successfully carried out in this work.

There is a large amount of remote sensing data available for land use and land cover (LULC) classification and thus optimizing selection of remote sensing variables is a great challenge. Although many methods such as Jeffreys–Matusita (JM) distance and random forests (RF) have been developed for this purpose, the existing methods ignore correlation and information duplication among remote sensing variables. In this study, a novel approach was proposed to improve the measures of potential class separability for the selection of remote sensing variables by taking into account correlations among the variables. The proposed method was examined with a total of thirteen spectral variables from a Gaofen-1 image, three class separability measures including JM distance, transformed divergence and B-distance and three classifiers including Bayesian discriminant (BD), Mahalanobis distance (MD) and RF for classification of six LULC types at the East Dongting Lake of Hunan, China. The results showed that (1) The proposed approach selected the first three spectral variables and resulted in statistically stable classification accuracies for three improved class separability measures. That is, the classification accuracies using three or more spectral variables statistically did not significantly differ from each other at a significant level of 0.05; (2) The statistically stable classification accuracies obtained by integrating MD and BD classifiers with the improved class separability measures were also statistically not significantly different from those by RF; (3) The numbers of the selected spectral variables using the improved class separability measures to create the statistically stable classification accuracies by MD and BD classifiers were much smaller than those from the original class separability measures and RF; and (4) Three original class separability measures and RF led to similar ranks of importance of the spectral variables, while the ranks achieved by the improved class separability measures were different due to the consideration of correlations among the variables. This indicated that the proposed method more effectively and quickly selected the spectral variables to produce the statistically stable classification accuracies compared with the original class separability measures and RF and thus improved the selection of the spectral variables for the classification.

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.

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.

We have implemented a post-processor called SPEECHPP to correct word-level errors committed by an arbitrary speech recognizer. Applying a noisy-channelmodel, SPEECHPPusesa Viterbi beam-search that employs language and channel models. Previous work demonstrated that a simple word-for-word channel model was sufficient to yield substantial increases in word accuracy. This paper demonstrates that some improvements in word accuracy result from augmenting the channel model with an account of word fertility in the channel. This work further demonstrates that a modern continuous speech recognizer can be used in "black-box" fashion for robustly recognizing speech for which the recognizer was not originally trained. This work also demonstrates that in the case where the recognizer canbe tuned to the new task, environment, or speaker, the post-processor can also contribute to performance improvements.

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.

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.

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.

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.

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.

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.

Hearing-impaired listeners often have great difficulty understanding speech in surroundings with background noise or reverberation. Based on array techniques, two microphone prototypes (broadside and endfire) have been developed with strongly directional characteristics [Soede et al., "Development of a new directional hearing instrument based on array technology," J. Acoust. Soc. Am. 94, 785-798 (1993)]. Physical measurements show that the arrays attenuate reverberant sound by 6 dB (free-field) and can improve the signal-to-noise ratio by 7 dB in a diffuse noise field (measured with a KEMAR manikin). For the clinical assessment of these microphones an experimental setup was made in a sound-insulated listening room with one loudspeaker in front of the listener simulating the partner in a discussion and eight loudspeakers placed on the edges of a cube producing a diffuse background noise. The hearing-impaired subject wearing his own (familiar) hearing aid is placed in the center of the cube. The speech-reception threshold in noise for simple Dutch sentences was determined with a normal single omnidirectional microphone and with one of the microphone arrays. The results of monaural listening tests with hearing impaired subjects show that in comparison with an omnidirectional hearing-aid microphone the broadside and endfire microphone array gives a mean improvement of the speech reception threshold in noise of 7.0 dB (26 subjects) and 6.8 dB (27 subjects), respectively. Binaural listening with two endfire microphone arrays gives a binaural improvement which is comparable to the binaural improvement obtained by listening with two normal ears or two conventional hearing aids.

The quadratic form can be expressed as a monotonically increasing sum of squares when the inverse covariance matrix is represented in canonical form. This formulation has the advantage that, in testing a particular class hypothesis, computations can be discontinued when the partial sum exceeds the smallest value obtained for other classes already tested. A method for channel selection is presented which arranges the original input measurements in that order which minimizes the expected number of computations. The classification algorithm was tested on data from LARS Flight Line Cl and found to reduce the sum-of-products operations by a factor of 6.7 compared to the conventional approach. In effect, the accuracy of a twelve-channel classification was achieved using only that CPU time required for a conventional four-channel classification.