Channel Detection Algorithm Research Articles

Assessing expert reliability in determining intracranial EEG channel quality and introducing the automated bad channel detection algorithm

Objective. To evaluate the inter- and intra-rater reliability for the identification of bad channels among neurologists, EEG Technologists, and naïve research personnel, and to compare their performance with the automated bad channel detection (ABCD) algorithm for detecting bad channels. Approach. Six Neurologists, ten EEG Technologists, and six naïve research personnel (22 raters in total) were asked to rate 1440 real intracranial EEG channels as good or bad. Intra- and interrater kappa statistics were calculated for each group. We then compared each group to the ABCD algorithm which uses spectral and temporal domain features to classify channels as good or bad. Main results. Analysis of channel ratings from our participants revealed variable intra-rater reliability within each group, with no significant differences across groups. Inter-rater reliability was moderate among neurologists and EEG Technologists but minimal among naïve participants. Neurologists demonstrated a slightly higher consistency in ratings than EEG Technologists. Both groups occasionally misclassified flat channels as good, and participants generally focused on low-frequency content for their assessments. The ABCD algorithm, in contrast, relied more on high-frequency content. A logistic regression model showed a linear relationship between the algorithm’s ratings and user responses for predominantly good channels, but less so for channels rated as bad. Sensitivity and specificity analyses further highlighted differences in rating patterns among the groups, with neurologists showing higher sensitivity and naïve personnel higher specificity. Significance. Our study reveals the bias in human assessments of intracranial electroencephalography (iEEG) data quality and the tendency of even experienced professionals to overlook certain bad channels, highlighting the need for standardized, unbiased methods. The ABCD algorithm, outperforming human raters, suggests the potential of automated solutions for more reliable iEEG interpretation and seizure characterization, offering a reliable approach free from human biases.

Adaptable and Robust EEG Bad Channel Detection Using Local Outlier Factor (LOF).

Electroencephalogram (EEG) data are typically affected by artifacts. The detection and removal of bad channels (i.e., with poor signal-to-noise ratio) is a crucial initial step. EEG data acquired from different populations require different cleaning strategies due to the inherent differences in the data quality, the artifacts’ nature, and the employed experimental paradigm. To deal with such differences, we propose a robust EEG bad channel detection method based on the Local Outlier Factor (LOF) algorithm. Unlike most existing bad channel detection algorithms that look for the global distribution of channels, LOF identifies bad channels relative to the local cluster of channels, which makes it adaptable to any kind of EEG. To test the performance and versatility of the proposed algorithm, we validated it on EEG acquired from three populations (newborns, infants, and adults) and using two experimental paradigms (event-related and frequency-tagging). We found that LOF can be applied to all kinds of EEG data after calibrating its main hyperparameter: the LOF threshold. We benchmarked the performance of our approach with the existing state-of-the-art (SoA) bad channel detection methods. We found that LOF outperforms all of them by improving the F1 Score, our chosen performance metric, by about 40% for newborns and infants and 87.5% for adults.

Quantized Turbo Equalization for Non-binary LDPC Coded Partial-Response Channels

In this paper, we consider iterative detection and decoding (i.e., turbo equalization) for nonbinary low-density parity-check (LDPC) coded partial-response channels, where a quantizer is present to discretize the continuous received signal. We propose a turbo equalizer that uses the pre-computed quantized channel transition probabilities in the symbol-level BCJR channel detection algorithm, which significantly reduces the computational complexity by avoiding real-time floating-point multiplications. The proposed approach is further extended to nonbinary LDPC coded bit-patterned media recording (BPMR) channels. Simulation results show that with a small number of quantization bits, the proposed receiver approaches closely the performance of the conventional turbo equalizer operating on unquantized signals.

Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%.

Performance Improvement in Wideband Spectrum Sensing Under Fading: Use of Diversity

Diversity is used to combat fading and shadowing in narrowband spectrum sensing. In this paper, we propose two new detection algorithms for wideband spectrum sensing that use square law combining (SLC) diversity to improve the detection performance. We analyze the performance of these algorithms under Nakagami fading. The proposed schemes include channel by channel square law combining (CC-SLC) and ranked square law combining (R-SLC). We first provide the asymptotic analysis for decision statistic of received energy to simplify the analysis. The approximated probability density function (pdf) to decision statistic is used to derive the pdf of received energy for P number of diversity branches. The performance of the proposed algorithms is measured in terms of probability of insufficient spectrum opportunity (P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ISO</sub> ) and probability of excessive interference opportunity (P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EIO</sub> ). The analysis provided in this paper is general and can be used with any fading model and diversity scheme. Experiments are carried out using theoretical analysis and also verified by using Monte-Carlo simulation. Our analysis shows that the proposed algorithms using diversity outperform the channel by channel and ranked channel detection algorithms used under no diversity. Our results also indicate that among the proposed detection algorithms R-SLC performs better.

Network Packet Length Covert Channel Based on Empirical Distribution Function

Network packet length covert channel modulates secret message bits onto the packet lengths to transmit secret messages. In this paper, a novel network packet length covert channel is proposed. The proposed scheme is based on the empirical distribution function of packet length series of legitimate traffic. Different from the existing schemes, the lengths of packets which are generated by the covert sender follow the distribution of normal traffic more closely in our scheme. To validate the security of the proposed scheme, the state-of-the-art packet length covert channel detection algorithm is adopted. The experimental results show that the packet length covert channel provides a significant performance improvement in detection resistance meanings.

Structural Determinants of Water Permeation through the Sodium-Galactose Transporter vSGLT

Sodium-glucose transporters (SGLTs) facilitate the movement of water across the cell membrane, playing a central role in cellular homeostasis. Here, we present a detailed analysis of the mechanism of water permeation through the inward-facing state of vSGLT based on nearly 10 μs of molecular dynamics simulations. These simulations reveal the transient formation of a continuous water channel through the transporter that permits water to permeate the protein. Trajectories in which spontaneous release of galactose is observed, as well as those in which galactose remains in the binding site, show that the permeation rate, although modulated by substrate occupancy, is not tightly coupled to substrate release. Using a, to our knowledge, novel channel-detection algorithm, we identify the key residues that control water flow through the transporter and show that solvent gating is regulated by side-chain motions in a small number of residues on the extracellular face. A sequence alignment reveals the presence of two insertion sites in mammalian SGLTs that flank these outer-gate residues. We hypothesize that the absence of these sites in vSGLT may account for the high water permeability values for vSGLT determined via simulation compared to the lower experimental estimates for mammalian SGLT1.

Insight into the Mechanism of Water Permeation through the Sodium-Galactose Transporter vSGLT from Long Molecular Dynamics Simulations

Sodium-glucose transporters (SGLTs) mediate sugar transport as well as water flow across the cell membrane. Using a series of long molecular dynamics simulations generated on the special-purpose Anton supercomputer totaling nearly 16 microseconds in aggregate, we investigated the mechanism of water permeation through the inward-facing state of the bacterial sugar transporter vSGLT from Vibrio parahaemolyticus. Our simulations reveal conformational changes in the extracellular gate that create a transient water channel through the transporter. We developed a novel channel detection algorithm to characterize water pathways through the protein and identify key residues that control water flow. The simulations contain multiple, independent instances in which the galactose, initially bound to the transporter, spontaneously exits to solution. By comparing water flow through vSGLT when the sugar is bound, exiting and free in solution, we show that water flow, while modulated by galactose occupancy, is not coupled to substrate release.

Capacity Bounds and Concatenated Codes over Segmented Deletion Channels

We develop an information theoretic characterization and a practical coding approach for segmented deletion channels. Compared to channels with independent and identically distributed (i.i.d.) deletions, where each bit is independently deleted with an equal probability, the segmentation assumption imposes certain constraints, i.e., in a block of bits of a certain length, only a limited number of deletions are allowed to occur. This channel model has recently been proposed and motivated by the fact that for practical systems, when a deletion error occurs, it is more likely that the next one will not appear very soon. We first argue that such channels are information stable, hence their channel capacity exists. Then, we introduce several upper and lower bounds with two different methods in an attempt to understand the channel capacity behavior. The first scheme utilizes certain information provided to the transmitter and/or receiver while the second one explores the asymptotic behavior of the bounds when the average bit deletion rate is small. In the second part of the paper, we consider a practical channel coding approach over a segmented deletion channel. Specifically, we utilize outer LDPC codes concatenated with inner marker codes, and develop suitable channel detection algorithms for this scenario. Different maximum-a-posteriori (MAP) based channel synchronization algorithms operating at the bit and symbol levels are introduced, and specific LDPC code designs are explored. Simulation results clearly indicate the advantages of the proposed approach. In particular, for the entire range of deletion probabilities less than unity, our scheme offers a significantly larger transmission rate compared to the other existing solutions in the literature.

Evaluation of detection algorithms for perpendicular recording channels with intertrack interference

Channel detection algorithms for handling intertrack interference (ITI) in perpendicular magnetic recording channels are studied in this paper. The goal is to optimize channel detection to attain the best possible performance for a practical system. Two channel detection models, namely, the single-track model and the joint-track model are evaluated using information rate analysis as well as simulations. Numerical results show that joint-track detection may be needed when ITI is severe.

Automatic segmentation and classification of ionic-channel signals

Identification of ionic-channel types and their selectivity depends critically on the open channel current that can be resolved. In this paper, an automatic channel detection algorithm is proposed that is based on sequential minimization of an index which is usually used in cluster analysis. The algorithm consists of two stages, namely segmentation and classification. In the first stage, the signal samples are segmented based on the assumption that the samples in each segment should be sequentially connected. In the second stage, the resultant segments are classified with no regard to their connectivities. Results on synthetic and real channel currents are very encouraging and they suggest that this algorithm will substantially increase the productivity of many laboratories involved in ionic-channel research.