Limited Number Of Electrodes Research Articles

Geometric neural network based on phase space for BCI-EEG decoding.

\textbf{Objective:} 
The integration of Deep Learning (DL) algorithms on brain signal analysis is still in its nascent stages compared to their success in fields like Computer Vision. This is particularly true for BCI, where the brain activity is decoded to control external devices without requiring muscle control.
Electroencephalography (EEG) is a widely adopted choice for designing BCI systems due to its non-invasive and cost-effective nature and excellent temporal resolution. Still, it comes at the expense of limited training data, poor signal-to-noise, and a large variability across and within-subject recordings. 
Finally, setting up a BCI system with many electrodes takes a long time, hindering the widespread adoption of reliable DL architectures in BCIs outside research laboratories. To improve adoption, we need to improve user comfort using, for instance, reliable algorithms that operate with few electrodes. 
\textbf{Approach:} Our research aims to develop a DL algorithm that delivers effective results with a limited number of electrodes. Taking advantage of the Augmented Covariance Method and the framework of SPDNet, we propose the \method{} architecture and analyze its performance and the interpretability of the results. The evaluation is conducted on 5-fold cross-validation, using only three electrodes positioned above the Motor Cortex. The methodology was tested on nearly 100 subjects from several open-source datasets using the Mother Of All BCI Benchmark (MOABB) framework. 
\textbf{Main results:} The results of our \method{} demonstrate that the augmented approach combined with the SPDNet significantly outperforms all the current state-of-the-art DL architecture in MI decoding. 
\textbf{Significance:} This new architecture is explainable and with a low number of trainable parameters.

High‐Resolution Cortical Dipole Imaging Using EEG Data Measured with a Small Number of Electrodes Based on the International 10–20 System

Cortical dipole imaging is a common technique utilized to visualize the electrical activity in the brain. However, this technique requires ~100 electrodes from a spatial resolution perspective, resulting in a long preparation time and a heavy burden on the subject. According to the international 10–20 system for recording electroencephalogram signals, 19 electrodes are placed on the scalp of the subject. In this study, high‐resolution cortical dipole imaging using the international 10–20 system was investigated for daily applications. The potential at any position on the scalp was interpolated using multiple regression analysis, and optimum parameter estimation was conducted using the proposed method. The simulation and experimental results suggest that the proposed interpolation method is effective for high‐resolution brain activity imaging when using a limited number of electrodes. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Design of Three-Dimensional Electrical Impedance Tomography System for Rock Samples

Research on the electrical properties of rocks and ores plays a crucial role in the development of geophysical electromagnetism methods. However, currently available instruments suffer from high power consumption, a limited number of electrodes, inaccurate measurements, poor portability, and a limited ability to measure the electrical parameters of rocks and ores. To address these issues, this paper presents a three-dimensional electrical impedance tomography system for rock samples with high-density microelectrodes based on an Android system and STM32 microcontroller. The system features high observation accuracy, dense electrode arrays (with 384 current and potential electrodes), flexible electrode selection, user-friendly human–computer interaction, good stability, and real-time performance. Powered by a single power bank, the entire instrument can be controlled and monitored wirelessly via Bluetooth and Wi-Fi technology using an Android smartphone. Additionally, the system not only enables accurate measurement of electrical parameters, but also facilitates the generation of three-dimensional impedance imaging of specimens via inversion algorithms after data export, allowing for a comprehensive understanding of the electrical properties of rocks and ores. This system holds great potential for future research in this field.

Interictal Electrical Source Imaging.

Interictal electrical source imaging (ESI) determines the neuronal generators of epileptic activity in EEG occurring outside of seizures. It uses computational models to take anatomic and neuronal characteristics of the individual patient into account. The presented article provides an overview of application and clinical value of interictal ESI in patients with pharmacoresistant focal epilepsies undergoing evaluation for surgery. Neurophysiological constraints of interictal data are discussed and technical considerations are summarized. Typical indications are covered as well as issues of integration into clinical routine. Finally, an outlook on novel markers of epilepsy for interictal source analysis is presented. Interictal ESI provides diagnostic performance on par with other established methods, such as MRI, PET, or SPECT. Although its accuracy benefits from high-density recordings, it provides valuable information already when applied to EEG with only a limited number of electrodes with complete coverage. Novel oscillatory markers and the integration of frequency coupling and connectivity may further improve accuracy and efficiency.

Electroencephalography source localization.

Electroencephalography (EEG) has been and is still widely used in brain function research. EEG has advantages over other neuroimaging modalities. First, it not only directly images the electrical activity of neurons; it has a higher temporal resolution. Furthermore, current advanced technologies enable accurate mathematical calculations and sophisticated localization from EEG data. Several important factors should be considered for EEG analysis using these advanced technologies. First, raw EEG data contain physiological or nonphysiological artifacts. Therefore, preprocessing methods and algorithms to detect and remove these artifacts have been proposed and developed. In the analysis of preprocessed EEG, forward and inverse problems require solving and several proposed models have been applied. To solve the forward problem, the source information and matrix parameters from which the EEG originates are essential. Therefore, an accurate head model is required. In contrast, the possible combinations of the current sources computed inversely from EEG measured at a limited number of electrodes are infinite, referring to the inverse problem. The inverse problem can be solved by setting limits based on assumptions made of the anatomy and physiology on the generation and propagation of the current sources. Thus, methods such as dipole source models and distributed source models have been proposed. Source localization requires the consideration of many factors such as the preprocessing of raw EEG data, artifact removal, accurate head models and forward problems, and inverse computation problems. This review summarizes the methods and considerations applied to the above EEG source localization process. It also introduces the applications of EEG source localization for epilepsy and other diseases as well as brain function studies and discusses future directions.

Towards optimizing electrode configurations for silent speech recognition based on high-density surface electromyography

Objective. Silent speech recognition (SSR) based on surface electromyography (sEMG) is an attractive non-acoustic modality of human-machine interfaces that convert the neuromuscular electrophysiological signals into computer-readable textual messages. The speaking process involves complex neuromuscular activities spanning a large area over the facial and neck muscles, thus the locations of the sEMG electrodes considerably affected the performance of the SSR system. However, most of the previous studies used only a quite limited number of electrodes that were placed empirically without prior quantitative analysis, resulting in uncertainty and unreliability of the SSR outcomes. Approach. In this study, the technique of high-density sEMG was proposed to provide a full representation of the articulatory muscle activities so that the optimal electrode configuration for SSR could be systemically explored. A total of 120 closely spaced electrodes were placed on the facial and neck muscles to collect the high-density sEMG signals for classifying ten digits (0–9) silently spoken in both English and Chinese. The sequential forward selection algorithm was adopted to explore the optimal electrodes configurations. Main Results. The results showed that the classification accuracy increased rapidly and became saturated quickly when the number of selected electrodes increased from 1 to 120. Using only ten optimal electrodes could achieve a classification accuracy of 86% for English and 94% for Chinese, whereas as many as 40 non-optimized electrodes were required to obtain comparable accuracies. Also, the optimally selected electrodes seemed to be mostly distributed on the neck instead of the facial region, and more electrodes were required for English recognition to achieve the same accuracy. Significance. The findings of this study can provide useful guidelines about electrode placement for developing a clinically feasible SSR system and implementing a promising approach of human-machine interface, especially for patients with speaking difficulties.

Improving the resolution of investigation using ERT instruments with a reduced number of electrodes

Since electrical resistivity tomography (ERT) is a standard geophysical method widely used in scientific and technological domains, many efforts have centered on improving its accuracy. The advantage of this method is its great capacity to combine and manage a high number of electrodes at the same time. A larger number of electrodes allows more possible array combinations and therefore, multiplies the number of acquisition points improving both horizontal and vertical resolution. However, in some cases, an instrument with a high capacity of electrodes is not available reducing the quality of the acquisitions. In this study, we present a new technique that improves the resolution of ERT using instrument with a limited number of electrodes. As happens with the roll-along technique, the proposed method allows to perform several concatenated profiles and summon all the data in a master profile for its inversion. This new technique improves resolution with new measures allowing to work with a reduced budget, low acquisition times and without the need of a big workforce in the field. To prove the capabilities of the method, synthetic, laboratory and field data were used to compare the results of the inversions to a previously known model using a statistical approach. The results show improvements of up to 80% compared to the same instrument used in a traditional way when only a limited instrument is available.

Development of a Hardware Circuit for Real-Time Acquisition of Brain Activity Using NI myDAQ

Electroencephalography (EEG) is the recording of electrical activity of the brain. The 10–20 system is the standard electrode location method used to acquire EEG data, which uses 21 electrodes to record the electrical activity of the brain. Patient preparation and correct electrode placement are important to obtain reliable outputs. The current 10–20 system consumes greater time for patient preparation and also causes discomfort due to a higher number of electrodes being used or wearing an uncomfortable cap. This paper focuses on reducing the number of electrodes, thus reducing patient discomfort as well as preparation time. Advancement in the field of hardware and software processing has led to the utilization of brain waves for communication between human and the computer. This work deals with EEG-based Brain–Machine Interface (BMI) intended for designing a portable single-channel EEG signal acquisition system. EEG signal was acquired using the data acquisition module [National Instruments (NI) myDAQ] and the signal was viewed in the NI Laboratory Virtual Instrument Engineering Workbench (LabVIEW) environment. It was observed that the peak-to-peak amplitude of alpha, beta and theta waves changes in accordance with the activity the subjects performed. Thus, the developed instrument was tested on 10 different subjects to acquire the alpha, beta and theta waves by performing different activities. From the results, it can be concluded that the developed system can be used for studying a person’s brain waves (alpha, beta and theta) based on the activity performed by the subject with a limited number of electrodes.

Rapid Objective Assessment of Contrast Sensitivity and Visual Acuity With Sweep Visual Evoked Potentials and an Extended Electrode Array

Purpose Sweep visual evoked potentials (sVEPs) provide an implicit, objective, and sensitive evaluation of low-level visual functions such as visual acuity and contrast sensitivity. For practical and traditional reasons, sVEPs in ophthalmologic examinations have usually been recorded over a single or a limited number of electrodes over the medial occipital region. Here we examined whether a higher density of recording electrodes improves the estimation of individual low-level visual thresholds with sVEPS, and to which extent such testing could be streamlined for clinical application. Methods To this end, we tested contrast sensitivity and visual acuity in 26 healthy adult volunteers with a 68-electrode electroencephalogram (EEG) system. Results While the most sensitive electrophysiologic response was found at the traditional medial occipital electrode Oz in a small majority of individuals, it was found at neighboring electrodes for the remaining participants. At the group level, lower spatial frequencies were also associated with right lateralized responses. More generally, visual function was evaluated more sensitively based on EEG recorded at the most sensitive electrode defined individually for each participant. Our data suggest that recording over seven posterior electrodes while limiting the testing session to less than 15 minutes ensures a sensitive and consistent estimation of acuity and contrast sensitivity threshold estimates in every individual. Conclusions The present study shows that sampling from a larger number of posterior scalp electrodes is relevant to optimize visual function assessment and could be achieved efficiently in the time-constrained clinical setting.

Compressed Optimization of Device Architectures for Semiconductor Quantum Devices

Recent advances in nanotechnology have enabled researchers to manipulate small collections of quantum mechanical objects with unprecedented accuracy. In semiconductor quantum dot qubits, this manipulation requires controlling the dot orbital energies, tunnel couplings, and the electron occupations. These properties all depend on the voltages placed on the metallic electrodes that define the device, whose positions are fixed once the device is fabricated. While there has been much success with small numbers of dots, as the number of dots grows, it will be increasingly useful to control these systems with as few electrode voltage changes as possible. Here, we introduce a protocol, which we call the Compressed Optimization of Device Architectures (CODA), in order to both efficiently identify sparse sets of voltage changes that control quantum systems, and to introduce a metric which can be used to compare device designs. As an example of the former, we apply this method to simulated devices with up to 100 quantum dots and show that CODA automatically tunes devices more efficiently than other common nonlinear optimizers. To demonstrate the latter, we determine the optimal lateral scale for a triple quantum dot, yielding a simulated device that can be tuned with small voltage changes on a limited number of electrodes.

Deviance detection is the dominant component of auditory contextual processing in the lateral superior temporal gyrus: A human ECoG study

Auditory contextual processing has been assumed to be based on a hierarchical structure consisting of the primary auditory cortex, superior temporal gyrus (STG), and frontal lobe. Recent invasive studies on mismatch negativity (MMN) have revealed functional segregation for auditory contextual processing such as neural adaptation in the primary auditory cortex and prediction in the frontal lobe. However, the role of the STG remains unclear. We obtained induced activity in the high gamma band as mismatch response (MMR), an electrocorticographic (ECoG) counterpart to scalp MMN, and the components of MMR by analyzing ECoG data from patients with refractory epilepsy in an auditory oddball task paradigm. We found that MMR localized mainly in the bilateral posterior STGs, and that deviance detection largely accounted for MMR. Furthermore, adaptation was identified in a limited number of electrodes on the superior temporal plane. Our findings reveal a mixed contribution of deviance detection and adaptation depending on location in the STG. Such spatial considerations could lead to further understanding of the pathophysiology of relevant psychiatric disorders.

Semiasynchronous BCI Using Wearable Two-Channel EEG

Motor imagery (MI)-based electroencephalogram can be modulated voluntarily by users through feedback provided by a brain–computer interface (BCI). In MI-based BCIs, feedback design plays an important role in MI training, especially in BCIs that use a limited number of electrodes. This paper presents a semiasynchronous MI-based BCI system that uses discrete and continuous feedback together, in order to improve practicability and training efficiency. Results show that the proposed method can reduce command generation time to 3.77 s with the accuracy of 77%, using our proposed continuous feedback with a two-channel MI-based BCI system. Our proposed system is noninvasive and wearable, featuring improvement in the practical utility, and operational convenience of a BCI system.

Evaluation of matrix factorisation approaches for muscle synergy extraction

The muscle synergy concept provides a widely-accepted paradigm to break down the complexity of motor control. In order to identify the synergies, different matrix factorisation techniques have been used in a repertoire of fields such as prosthesis control and biomechanical and clinical studies. However, the relevance of these matrix factorisation techniques is still open for discussion since there is no ground truth for the underlying synergies. Here, we evaluate factorisation techniques and investigate the factors that affect the quality of estimated synergies. We compared commonly used matrix factorisation methods: Principal component analysis (PCA), Independent component analysis (ICA), Non-negative matrix factorization (NMF) and second-order blind identification (SOBI). Publicly available real data were used to assess the synergies extracted by each factorisation method in the classification of wrist movements. Synthetic datasets were utilised to explore the effect of muscle synergy sparsity, level of noise and number of channels on the extracted synergies. Results suggest that the sparse synergy model and a higher number of channels would result in better estimated synergies. Without dimensionality reduction, SOBI showed better results than other factorisation methods. This suggests that SOBI would be an alternative when a limited number of electrodes is available but its performance was still poor in that case. Otherwise, NMF had the best performance when the number of channels was higher than the number of synergies. Therefore, NMF would be the best method for muscle synergy extraction.

Rapid Objective Assessment of Contrast Sensitivity and Visual Acuity With Sweep Visual Evoked Potentials and an Extended Electrode Array.

Sweep visual evoked potentials (sVEPs) provide an implicit, objective, and sensitive evaluation of low-level visual functions such as visual acuity and contrast sensitivity. For practical and traditional reasons, sVEPs in ophthalmologic examinations have usually been recorded over a single or a limited number of electrodes over the medial occipital region. Here we examined whether a higher density of recording electrodes improves the estimation of individual low-level visual thresholds with sVEPS, and to which extent such testing could be streamlined for clinical application. To this end, we tested contrast sensitivity and visual acuity in 26 healthy adult volunteers with a 68-electrode electroencephalogram (EEG) system. While the most sensitive electrophysiologic response was found at the traditional medial occipital electrode Oz in a small majority of individuals, it was found at neighboring electrodes for the remaining participants. At the group level, lower spatial frequencies were also associated with right lateralized responses. More generally, visual function was evaluated more sensitively based on EEG recorded at the most sensitive electrode defined individually for each participant. Our data suggest that recording over seven posterior electrodes while limiting the testing session to less than 15 minutes ensures a sensitive and consistent estimation of acuity and contrast sensitivity threshold estimates in every individual. The present study shows that sampling from a larger number of posterior scalp electrodes is relevant to optimize visual function assessment and could be achieved efficiently in the time-constrained clinical setting.

Recognition of a Virtual Maze Scene Using Simulated Prosthetic Vision.

A real-time psychological physics experimental platform was built to aid in discovering the best real-time image processing strategy for the limited number of electrodes, and in providing the most useful prosthetic implant visual information. A number of maze pathfinder tasks were performed at low resolution to simulate use by blind individuals in performing daily visual activities. In this study, simple (5 rows × 5 columns), medium (8 × 8), and complex (11 × 11) maze models were created in 3DMAX. The models were constructed by using Unity to build virtual scenes for real-time pixel processing, including binarization, color inversion, and matching to templates with three different resolutions (16 × 16, 24 × 24, 32 × 32). Subjects completed maze pathfinding tasks using 45° and 60° views of the labyrinth at the 32 × 32 resolution to determine the optimal viewing angle. The time required to find the maze entrance and complete the maze were analyzed along with the rate of maze completion (accuracy) at various resolutions. In the first experiment, the average time required to find the entrance and the average maze pathfinding duration were significantly longer at 60°. Therefore, the 45° view provided the best perspective. In the second experiment, the angle was fixed to 45°. As the maze difficulty increased, the average time needed to find the maze entrance decreased, but the average time required to complete the maze increased. When the difficulty of the maze was fixed, the time required to find the maze entrance and solve the maze decreased when the resolution increased. The accuracy with which the maze path was identified increased as well. The average maze pathfinding time at 24 × 24 was significantly less than at 16 × 16. A similar trend was observed when the average maze pathfinding times at 32 × 32 and 24 × 24 were compared. At 32 × 32, the average pathfinding accuracy was 100%. This indicates that 32 × 32 is an effective resolution for maze pathfinding.

Proportional estimation of finger movements from high-density surface electromyography.

BackgroundThe importance to restore the hand function following an injury/disease of the nervous system led to the development of novel rehabilitation interventions. Surface electromyography can be used to create a user-driven control of a rehabilitation robot, in which the subject needs to engage actively, by using spared voluntary activation to trigger the assistance of the robot.MethodsThe study investigated methods for the selective estimation of individual finger movements from high-density surface electromyographic signals (HD-sEMG) with minimal interference between movements of other fingers. Regression was evaluated in online and offline control tests with nine healthy subjects (per test) using a linear discriminant analysis classifier (LDA), a common spatial patterns proportional estimator (CSP-PE), and a thresholding (THR) algorithm. In all tests, the subjects performed an isometric force tracking task guided by a moving visual marker indicating the contraction type (flexion/extension), desired activation level and the finger that should be moved. The outcome measures were mean square error (nMSE) between the reference and generated trajectories normalized to the peak-to-peak value of the reference, the classification accuracy (CA), the mean amplitude of the false activations (MAFA) and, in the offline tests only, the Pearson correlation coefficient (PCORR).ResultsThe offline tests demonstrated that, for the reduced number of electrodes (≤24), the CSP-PE outperformed the LDA with higher precision of proportional estimation and less crosstalk between the movement classes (e.g., 8 electrodes, median MAFA ~ 0.6 vs. 1.1 %, median nMSE ~ 4.3 vs. 5.5 %). The LDA and the CSP-PE performed similarly in the online tests (median nMSE < 3.6 %, median MAFA < 0.7 %), but the CSP-PE provided a more stable performance across the tested conditions (less improvement between different sessions). Furthermore, THR, exploiting topographical information about the single finger activity from HD-sEMG, provided in many cases a regression accuracy similar to that of the pattern recognition techniques, but the performance was not consistent across subjects and fingers.ConclusionsThe CSP-PE is a method of choice for selective individual finger control with the limited number of electrodes (<24), whereas for the higher resolution of the recording, either method (CPS-PA or LDA) can be used with a similar performance. Despite the abundance of detection points, the simple THR showed to be significantly worse compared to both pattern recognition/regression methods. Nevertheless, THR is a simple method to apply (no training), and it could still give satisfactory performance in some subjects and/or simpler scenarios (e.g., control of selected fingers). These conclusions are important for guiding future developments towards the clinical application of the methods for individual finger control in rehabilitation robotics.

Moving object detection and background enhancement for thalamic visual prostheses.

Visual prostheses open the door of hope to restore functional vision for the blind. One of the main challenges facing their development is the limited number of electrodes used in the stimulation process which limits the resolution of the perceived images. To improve the perception, the useful features in the scene need to be enhanced while the other features should be suppressed to achieve better resolution. This paper introduces an image processing method to enhance three main features detectable by the natural visual pathway; namely the contrast, the motion and the edges. It then reduces the size of the image into an activity matrix used to generate the electric stimulation for the electrodes array. We compared the proposed method to four other image processing strategies in terms of the quality of the resulting image in addition to the perceived image using a simulation of prosthetic vision. Results demonstrate that the proposed method outperforms the other techniques in both aspects.

An attempt to localize brain electrical activity sources using EEG with limited number of electrodes

A very interesting research goal is to find underlying sources generating the EEG signal – referred to as the “EEG inverse problem”. Its aim is to determine spatial distribution of brain activity, described by local brain currents density, on the basis of potentials measured on the scalp as EEG signal. The purpose of the research presented in the article was to check whether the results of the inverse problem solution, obtained by the LORETA algorithm for the reduced set of 8 electrodes selected by the authors will be close to the results for the initial set of 32 electrodes. EEG signals were registered during the BCI operation based on ERD/ERS potentials. Obtained results showed no significant differences in the location of the most important sources in both cases. It is worth emphasizing that reducing the number of electrodes would have a significant impact on an BCI ergonomics.

Non-invasive focus localization, right ventricular epicardial potential mapping in patients with an MRI-conditional pacemaker system - a pilot study.

BackgroundWith the advent of magnetic resonance imaging (MRI) conditional pacemaker systems, the possibility of performing MRI in pacemaker patients has been introduced. Besides for the detailed evaluation of atrial and ventricular volumes and function, MRI can be used in combination with body surface potential mapping (BSPM) in a non-invasive inverse potential mapping (IPM) strategy. In non-invasive IPM, epicardial potentials are reconstructed from recorded body surface potentials (BSP). In order to investigate whether an IPM method with a limited number of electrodes could be used for the purpose of non-invasive focus localization, it was applied in patients with implanted pacing devices. Ventricular paced beats were used to simulate ventricular ectopic foci.MethodsTen patients with an MRI-conditional pacemaker system and a structurally normal heart were studied. Patient-specific 3D thorax volume models were reconstructed from the MRI images. BSP were recorded during ventricular pacing. Epicardial potentials were inversely calculated from the BSP. The site of epicardial breakthrough was compared to the position of the ventricular lead tip on MRI and the distance between these points was determined.ResultsFor all patients, the site of earliest epicardial depolarization could be identified. When the tip of the pacing lead was implanted in vicinity to the epicardium, i.e. right ventricular (RV) apex or RV outflow tract, the distance between lead tip position and epicardial breakthrough was 6.0 ± 1.9 mm.ConclusionsIn conclusion, the combined MRI and IPM method is clinically applicable and can identify sites of earliest depolarization with a clinically useful accuracy.Electronic supplementary materialThe online version of this article (doi:10.1007/s10840-015-0054-9) contains supplementary material, which is available to authorized users.

Atrial signal extraction in atrial fibrillation electrocardiograms using a tensor decomposition approach.

Atrial fibrillation (AF) is the most common cardiac arrhythmia encountered in clinical practice and remains a major challenge in cardiology. The noninvasive analysis of AF usually requires the estimation of the atrial activity (AA) signal in surface electrocardiogram (ECG) recordings. The present contribution puts forward a tensor decomposition approach for noninvasive AA extraction in AF ECG recordings. As opposed to the matrix approach, tensor decompositions are generally unique under mild conditions and have the potential to perform source separation in scenarios with a limited number of electrodes. An experimental study on a synthethic signal model and a real AF ECG recording evaluates the performance of the so-called block term tensor decomposition approach as compared to matrix techniques such as principal component analysis and independent component analysis.