scholarly journals Four methods of brain pattern analyses of fMRI signals associated with wrist extension versus wrist flexion studied for potential use in future motor learning BCI

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0254338
Author(s):  
Aniruddh Ravindran ◽  
Jake D. Rieke ◽  
Jose Daniel Alcantara Zapata ◽  
Keith D. White ◽  
Avi Matarasso ◽  
...  
Keyword(s):  
Brain Activity ◽  
Stroke Survivor ◽  
Healthy Adults ◽  
Blood Oxygenation ◽  
Fmri Data ◽  
Support Vector ◽  
Wrist Flexion ◽  
Wrist Extension ◽  
Significant Difference ◽  
Neural Feedback

Objective In stroke survivors, a treatment-resistant problem is inability to volitionally differentiate upper limb wrist extension versus flexion. When one intends to extend the wrist, the opposite occurs, wrist flexion, rendering the limb non-functional. Conventional therapeutic approaches have had limited success in achieving functional recovery of patients with chronic and severe upper extremity impairments. Functional magnetic resonance imaging (fMRI) neurofeedback is an emerging strategy that has shown potential for stroke rehabilitation. There is a lack of information regarding unique blood-oxygenation-level dependent (BOLD) cortical activations uniquely controlling execution of wrist extension versus uniquely controlling wrist flexion. Therefore, a first step in providing accurate neural feedback and training to the stroke survivor is to determine the feasibility of classifying (or differentiating) brain activity uniquely associated with wrist extension from that of wrist flexion, first in healthy adults. Approach We studied brain signal of 10 healthy adults, who performed wrist extension and wrist flexion during fMRI data acquisition. We selected four types of analyses to study the feasibility of differentiating brain signal driving wrist extension versus wrist flexion, as follows: 1) general linear model (GLM) analysis; 2) support vector machine (SVM) classification; 3) ‘Winner Take All’; and 4) Relative Dominance. Results With these four methods and our data, we found that few voxels were uniquely active during either wrist extension or wrist flexion. SVM resulted in only minimal classification accuracies. There was no significant difference in activation magnitude between wrist extension versus flexion; however, clusters of voxels showed extension signal > flexion signal and other clusters vice versa. Spatial patterns of activation differed among subjects. Significance We encountered a number of obstacles to obtaining clear group results in healthy adults. These obstacles included the following: high variability across healthy adults in all measures studied; close proximity of uniquely active voxels to voxels that were common to both the extension and flexion movements; in general, higher magnitude of signal for the voxels common to both movements versus the magnitude of any given uniquely active voxel for one type of movement. Our results indicate that greater precision in imaging will be required to develop a truly effective method for differentiating wrist extension versus wrist flexion from fMRI data.

scholarly journals Portable Drowsiness Detection through Use of a Prefrontal Single-Channel Electroencephalogram

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4477 ◽  
Author(s):  
Mikito Ogino ◽  
Yasue Mitsukura
Keyword(s):  
Pattern Recognition ◽  
Classification Accuracy ◽  
Single Channel ◽  
Detection System ◽  
Brain Activity ◽  
Multiscale Entropy ◽  
Support Vector ◽  
Time Averaging ◽  
Drowsiness Detection ◽  
Significant Difference

Drowsiness detection has been studied in the context of evaluating products, assessing driver alertness, and managing office environments. Drowsiness level can be readily detected through measurement of human brain activity. The electroencephalogram (EEG), a device whose application relies on adhering electrodes to the scalp, is the primary method used to monitor brain activity. The many electrodes and wires required to perform an EEG place considerable constraints on the movement of users, and the cost of the device limits its availability. For these reasons, conventional EEG devices are not used in practical studies and businesses. Many potential practical applications could benefit from the development of a wire-free, low-priced device; however, it remains to be elucidated whether portable EEG devices can be used to estimate human drowsiness levels and applied within practical research settings and businesses. In this study, we outline the development of a drowsiness detection system that makes use of a low-priced, prefrontal single-channel EEG device and evaluate its performance in an offline analysis and a practical experiment. Firstly, for the development of the system, we compared three feature extraction methods: power spectral density (PSD), autoregressive (AR) modeling, and multiscale entropy (MSE) for detecting characteristics of an EEG. In order to efficiently select a meaningful PSD, we utilized step-wise linear discriminant analysis (SWLDA). Time-averaging and robust-scaling were used to fit the data for pattern recognition. Pattern recognition was performed by a support vector machine (SVM) with a radial basis function (RBF) kernel. The optimal hyperparameters for the SVM were selected by the grind search method so as to increase drowsiness detection accuracy. To evaluate the performance of the detections, we calculated classification accuracy using the SVM through 10-fold cross-validation. Our model achieved a classification accuracy of 72.7% using the PSD with SWLDA and the SVM. Secondly, we conducted a practical study using the system and evaluated its performance in a practical situation. There was a significant difference (* p < 0.05) between the drowsiness-evoked task and concentration-needed task. Our results demonstrate the efficacy of our low-priced portable drowsiness detection system in quantifying drowsy states. We anticipate that our system will be useful to practical studies with aims as diverse as measurement of classroom mental engagement, evaluation of movies, and office environment evaluation.

scholarly journals Classifying the Epilepsy Based on the Phase Space Sorted With the Radial Poincaré Sections in Electroencephalography

2021 ◽  
Vol 7 (2) ◽  
pp. 60-73
Author(s):  
Reyhaneh Zarifiyan Irani Nezhad ◽  
◽  
Ghasem Sadeghi Bajestani ◽  
Reza Yaghoobi Karimui ◽  
Behnaz Sheikholeslami1 ◽  
...  
Keyword(s):  
Phase Space ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Computational Method ◽  
Statistical Characteristics ◽  
Support Vector ◽  
Brain Disorder ◽  
Poincaré Sections ◽  
Significant Difference ◽  
Poincare Sections

Background: Epilepsy is a brain disorder that changes the basin geometry of the oscillation of trajectories in the phase space. Nevertheless, recent studies on epilepsy often used the statistical characteristics of this space to diagnose epileptic seizures. Objectives: We evaluated changes caused by the seizures on the mentioned basin by focusing on phase space sorted by Poincaré sections. Materials & Methods: In this non-interventional clinical study (observational), 19 patients with generalized epilepsy were referred to the Epilepsy Department of Razavi Hospital (Mashhad, Iran) between 2018 and 2020, which their disease had been controlled after diagnosis and surgery. In evaluating the effects of this disorder on the oscillation basin of the EEG trajectories, we used the MATLAB@ R2019 software. In this computational method, we sorted the phase space reconstructed from the trajectories by using the radial Poincaré sections and then extracted a set of the geometric features. Finally, we detected the normal, pre-ictal, and ictal modes using a decision tree based on the Support Vector Machine (SVM) developed by features selected by a genetic algorithm. Results: The proposed method provided an accuracy of 94.96% for the three classes, which confirms the change in the oscillation basin of the trajectories. Analyzing the features by using t test also showed a significant difference between the three modes. Conclusion: The findings prove that epilepsy increases the oscillations basin of brain activity, but classification based on the segment cannot be applicable in clinical settings.

scholarly journals Recording site placement on planar silicon-based probes affects signal quality in acute neuronal recordings

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Richárd Fiáth ◽  
Domokos Meszéna ◽  
Zoltán Somogyvári ◽  
Mihály Boda ◽  
Péter Barthó ◽  
...  
Keyword(s):  
Single Unit ◽  
Brain Activity ◽  
Amplitude Distribution ◽  
High Amplitude ◽  
Neural Implants ◽  
Recording Site ◽  
Significant Difference ◽  
Planar Silicon ◽  
Silicon Based ◽  
Unit Yield

AbstractMultisite, silicon-based probes are widely used tools to record the electrical activity of neuronal populations. Several physical features of these devices are designed to improve their recording performance. Here, our goal was to investigate whether the position of recording sites on the silicon shank might affect the quality of the recorded neural signal in acute experiments. Neural recordings obtained with five different types of high-density, single-shank, planar silicon probes from anesthetized rats were analyzed. Wideband data were filtered to extract spiking activity, then the amplitude distribution of samples and quantitative properties of the recorded brain activity (single unit yield, spike amplitude and isolation distance) were compared between sites located at different positions of the silicon shank, focusing particularly on edge and center sites. Edge sites outperformed center sites: for all five probe types there was a significant difference in the signal power computed from the amplitude distributions, and edge sites recorded significantly more large amplitude samples both in the positive and negative range. Although the single unit yield was similar between site positions, the difference in spike amplitudes was noticeable in the range corresponding to high-amplitude spikes. Furthermore, the advantage of edge sites slightly decreased with decreasing shank width. Our results might aid the design of novel neural implants in enhancing their recording performance by identifying more efficient recording site placements.

Distinguishing hypochondriasis and schizophrenia using regional homogeneity: a resting-state fMRI study and support vector machine analysis

2021 ◽  
pp. 1-29
Author(s):  
Kangyu Jin ◽  
Zhe Shen ◽  
Guoxun Feng ◽  
Zhiyong Zhao ◽  
Jing Lu ◽  
...  
Keyword(s):  
Cognitive Function ◽  
Resting State ◽  
Parietal Lobe ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Regional Homogeneity ◽  
Middle Temporal Gyrus ◽  
Support Vector ◽  
Fmri Study

Abstract Objective: A few former studies suggested there are partial overlaps in abnormal brain structure and cognitive function between Hypochondriasis (HS) and schizophrenia (SZ). But their differences in brain activity and cognitive function were unclear. Methods: 21 HS patients, 23 SZ patients, and 24 healthy controls (HC) underwent Resting-state functional magnetic resonance imaging (rs-fMRI) with the regional homogeneity analysis (ReHo), subsequently exploring the relationship between ReHo value and cognitive functions. The support vector machines (SVM) were used on effectiveness evaluation of ReHo for differentiating HS from SZ. Results: Compared with HC, HS showed significantly increased ReHo values in right middle temporal gyrus (MTG), left inferior parietal lobe (IPL) and right fusiform gyrus (FG), while SZ showed increased ReHo in left insula, decreased ReHo values in right paracentral lobule. Additionally, HS showed significantly higher ReHo values in FG, MTG and left paracentral lobule but lower in insula than SZ. The higher ReHo values in insula were associated with worse performance in MCCB in HS group. SVM analysis showed a combination of the ReHo values in insula and FG was able to satisfactorily distinguish the HS and SZ patients. Conclusion: our results suggested the altered default mode network (DMN), of which abnormal spontaneous neural activity occurs in multiple brain regions, might play a key role in the pathogenesis of HS, and the resting-state alterations of insula closely related to cognitive dysfunction in HS. Furthermore, the combination of the ReHo in FG and insula was a relatively ideal indicator to distinguish HS from SZ.

COMPARISON OF ICTAL AND INTERICTAL EEG SIGNALS USING FRACTAL FEATURES

2013 ◽  
Vol 23 (06) ◽  
pp. 1350028 ◽  
Author(s):  
YU WANG ◽  
WEIDONG ZHOU ◽  
QI YUAN ◽  
XUELI LI ◽  
QINGFANG MENG ◽  
...  
Keyword(s):  
Feature Analysis ◽  
Support Vector ◽  
Eeg Analysis ◽  
Eeg Signals ◽  
Eeg Classification ◽  
Significant Difference ◽  
Interictal Eeg ◽  
Nonlinear Features ◽  
The Difference ◽  
Multi Scales

The feature analysis of epileptic EEG is very significant in diagnosis of epilepsy. This paper introduces two nonlinear features derived from fractal geometry for epileptic EEG analysis. The features of blanket dimension and fractal intercept are extracted to characterize behavior of EEG activities, and then their discriminatory power for ictal and interictal EEGs are compared by means of statistical methods. It is found that there is significant difference of the blanket dimension and fractal intercept between interictal and ictal EEGs, and the difference of the fractal intercept feature between interictal and ictal EEGs is more noticeable than the blanket dimension feature. Furthermore, these two fractal features at multi-scales are combined with support vector machine (SVM) to achieve accuracies of 97.58% for ictal and interictal EEG classification and 97.13% for normal, ictal and interictal EEG classification.

scholarly journals Peripheral BDNF: a candidate biomarker of healthy neural activity during learning is disrupted in schizophrenia

2014 ◽  
Vol 45 (4) ◽  
pp. 841-854 ◽  
Author(s):  
A. J. Skilleter ◽  
C. S. Weickert ◽  
A. Vercammen ◽  
R. Lenroot ◽  
T. W. Weickert
Keyword(s):  
Functional Neuroimaging ◽  
Brain Activity ◽  
Healthy Adults ◽  
Enzyme Linked Immunosorbent Assay ◽  
Association Learning ◽  
Healthy Humans ◽  
Important Regulator ◽  
Learning Test ◽  
Parietal Cortices ◽  
And Task

Background.Brain-derived neurotrophic factor (BDNF) is an important regulator of synaptogenesis and synaptic plasticity underlying learning. However, a relationship between circulating BDNF levels and brain activity during learning has not been demonstrated in humans. Reduced brain BDNF levels are found in schizophrenia and functional neuroimaging studies of probabilistic association learning in schizophrenia have demonstrated reduced activity in a neural network that includes the prefrontal and parietal cortices and the caudate nucleus. We predicted that brain activity would correlate positively with peripheral BDNF levels during probabilistic association learning in healthy adults and that this relationship would be altered in schizophrenia.Method.Twenty-five healthy adults and 17 people with schizophrenia or schizo-affective disorder performed a probabilistic association learning test during functional magnetic resonance imaging (fMRI). Plasma BDNF levels were measured by enzyme-linked immunosorbent assay (ELISA).Results.We found a positive correlation between circulating plasma BDNF levels and brain activity in the parietal cortex in healthy adults. There was no relationship between plasma BDNF levels and task-related activity in the prefrontal, parietal or caudate regions in schizophrenia. A direct comparison of these relationships between groups revealed a significant diagnostic difference.Conclusions.This is the first study to show a relationship between peripheral BDNF levels and cortical activity during learning, suggesting that plasma BDNF levels may reflect learning-related brain activity in healthy humans. The lack of relationship between plasma BDNF and task-related brain activity in patients suggests that circulating blood BDNF may not be indicative of learning-dependent brain activity in schizophrenia.

Encoding of graded changes in spatial specificity of prior cues in human visual cortex

2014 ◽  
Vol 112 (11) ◽  
pp. 2834-2849 ◽  
Author(s):  
Yuko Hara ◽  
Justin L. Gardner
Keyword(s):  
Prior Information ◽  
Prior Probability ◽  
Blood Oxygenation ◽  
Response Magnitude ◽  
Behavioral Performance ◽  
Significant Difference ◽  
Cortical Responses ◽  
Sensory Responses ◽  
Contrast Discrimination ◽  
Spatial Specificity

Prior information about the relevance of spatial locations can vary in specificity; a single location, a subset of locations, or all locations may be of potential importance. Using a contrast-discrimination task with four possible targets, we asked whether performance benefits are graded with the spatial specificity of a prior cue and whether we could quantitatively account for behavioral performance with cortical activity changes measured by blood oxygenation level-dependent (BOLD) imaging. Thus we changed the prior probability that each location contained the target from 100 to 50 to 25% by cueing in advance 1, 2, or 4 of the possible locations. We found that behavioral performance (discrimination thresholds) improved in a graded fashion with spatial specificity. However, concurrently measured cortical responses from retinotopically defined visual areas were not strictly graded; response magnitude decreased when all 4 locations were cued (25% prior probability) relative to the 100 and 50% prior probability conditions, but no significant difference in response magnitude was found between the 100 and 50% prior probability conditions for either cued or uncued locations. Also, although cueing locations increased responses relative to noncueing, this cue sensitivity was not graded with prior probability. Furthermore, contrast sensitivity of cortical responses, which could improve contrast discrimination performance, was not graded. Instead, an efficient-selection model showed that even if sensory responses do not strictly scale with prior probability, selection of sensory responses by weighting larger responses more can result in graded behavioral performance benefits with increasing spatial specificity of prior information.

scholarly journals Neural Correlates of Feedback Processing in Toddlers

2014 ◽  
Vol 26 (7) ◽  
pp. 1519-1527 ◽  
Author(s):  
Marlene Meyer ◽  
Harold Bekkering ◽  
Denise J. C. Janssen ◽  
Ellen R. A. de Bruijn ◽  
Sabine Hunnius
Keyword(s):  
Early Childhood ◽  
Brain Development ◽  
Brain Activity ◽  
Feedback System ◽  
Brain Regions ◽  
Adaptive Performance ◽  
Feedback Processing ◽  
Essential Information ◽  
Electrophysiological Studies ◽  
Neural Feedback

External feedback provides essential information for successful learning. Feedback is especially important for learning in early childhood, as toddlers strongly rely on external signals to determine the consequences of their actions. In adults, many electrophysiological studies have elucidated feedback processes using a neural marker called the feedback-related negativity (FRN). The neural generator of the FRN is assumed to be the ACC, located in medial frontal cortex. As frontal brain regions are the latest to mature during brain development, it is unclear when in early childhood a functional feedback system develops. Is feedback differentiated on a neural level in toddlers and in how far is neural feedback processing related to children's behavioral adjustment? In an EEG experiment, we addressed these questions by measuring the brain activity and behavioral performance of 2.5-year-old toddlers while they played a feedback-guided game on a touchscreen. Electrophysiological results show differential brain activity for feedback with a more negative deflection for incorrect than correct outcomes, resembling the adult FRN. This provides the first neural evidence for feedback processing in toddlers. Notably, FRN amplitudes were predictive of adaptive behavior: the stronger the differential brain activity for feedback, the better the toddlers' adaptive performance during the game. Thus, already in early childhood toddlers' feedback-guided performance directly relates to the functionality of their neural feedback processing. Implications for early feedback-based learning as well as structural and functional brain development are discussed.

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