scholarly journals Information Theoretic Approaches for Motor-Imagery BCI Systems: Review and Experimental Comparison

Entropy ◽  
2018 ◽  
Vol 20 (1) ◽  
pp. 7 ◽  
Author(s):  
Rubén Martín-Clemente ◽  
Javier Olias ◽  
Deepa Thiyam ◽  
Andrzej Cichocki ◽  
Sergio Cruces
Keyword(s):  
Experimental Comparison ◽  
Information Theoretic ◽  
Computer Interfaces ◽  
Eeg Data ◽  
Leibler Divergence ◽  
The Common ◽  
Alpha Beta ◽  
Class Labels ◽  
The Relationship ◽  
Electroencephalogram Eeg

Brain computer interfaces (BCIs) have been attracting a great interest in recent years. The common spatial patterns (CSP) technique is a well-established approach to the spatial filtering of the electroencephalogram (EEG) data in BCI applications. Even though CSP was originally proposed from a heuristic viewpoint, it can be also built on very strong foundations using information theory. This paper reviews the relationship between CSP and several information-theoretic approaches, including the Kullback–Leibler divergence, the Beta divergence and the Alpha-Beta log-det (AB-LD)divergence. We also revise other approaches based on the idea of selecting those features that are maximally informative about the class labels. The performance of all the methods will be also compared via experiments.

EEG Analysis of Imagined Speech

2020 ◽  
pp. 679-692
Author(s):  
Sadaf Iqbal ◽  
Muhammed Shanir P.P. ◽  
Yusuf Uzzaman Khan ◽  
Omar Farooq
Keyword(s):  
Mean Absolute Deviation ◽  
Eeg Analysis ◽  
Absolute Deviation ◽  
Scalp Eeg ◽  
Computer Interfaces ◽  
Eeg Data ◽  
Scalp Electroencephalogram ◽  
Electroencephalogram Eeg ◽  
Control State ◽  
Excellent Tool

Scalp electroencephalogram (EEG) is one of the most commonly used methods to acquire EEG data for brain-computer interfaces (BCIs). Worldwide a large number of people suffer from disabilities which impair normal communication. Communication BCIs are an excellent tool which helps the affected patients communicate with others. In this paper scalp EEG data is analysed to discriminate between the imagined vowel sounds /a/, /u/ and no action or rest as control state. Mean absolute deviation (MAD) and Arithmetic mean are used as features to classify data into one of the classes /a/, /u/ or rest. With high classification accuracies of 87.5-100% for two class problem and 78.33-96.67% for three class problem that have been obtained in this work, this algorithm can be used in communication BCIs, to develop speech prosthesis and in synthetic telepathy systems.

EEG Analysis of Imagined Speech

2016 ◽  
Vol 3 (2) ◽  
pp. 32-44
Author(s):  
Sadaf Iqbal ◽  
Muhammed Shanir P.P. ◽  
Yusuf Uzzaman Khan ◽  
Omar Farooq
Keyword(s):  
Mean Absolute Deviation ◽  
Eeg Analysis ◽  
Absolute Deviation ◽  
Scalp Eeg ◽  
Computer Interfaces ◽  
Eeg Data ◽  
Scalp Electroencephalogram ◽  
Electroencephalogram Eeg ◽  
Control State ◽  
Excellent Tool

Scalp electroencephalogram (EEG) is one of the most commonly used methods to acquire EEG data for brain-computer interfaces (BCIs). Worldwide a large number of people suffer from disabilities which impair normal communication. Communication BCIs are an excellent tool which helps the affected patients communicate with others. In this paper scalp EEG data is analysed to discriminate between the imagined vowel sounds /a/, /u/ and no action or rest as control state. Mean absolute deviation (MAD) and Arithmetic mean are used as features to classify data into one of the classes /a/, /u/ or rest. With high classification accuracies of 87.5-100% for two class problem and 78.33-96.67% for three class problem that have been obtained in this work, this algorithm can be used in communication BCIs, to develop speech prosthesis and in synthetic telepathy systems.

scholarly journals Long-Term EEG Component Analysis Method Based on Lasso Regression

Algorithms ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 271
Author(s):  
Hongjian Bo ◽  
Haifeng Li ◽  
Boying Wu ◽  
Hongwei Li ◽  
Lin Ma
Keyword(s):  
Lasso Regression ◽  
Analysis Method ◽  
Acoustic Features ◽  
Cognitive Studies ◽  
Eeg Data ◽  
The Difference ◽  
The Relationship ◽  
Electroencephalogram Eeg ◽  
Good Agreement

At present, there are very few analysis methods for long-term electroencephalogram (EEG) components. Temporal information is always ignored by most of the existing techniques in cognitive studies. Therefore, a new analysis method based on time-varying characteristics was proposed. First of all, a regression model based on Lasso was proposed to reveal the difference between acoustics and physiology. Then, Permutation Tests and Gaussian fitting were applied to find the highest correlation. A cognitive experiment based on 93 emotional sounds was designed, and the EEG data of 10 volunteers were collected to verify the model. The 48-dimensional acoustic features and 428 EEG components were extracted and analyzed together. Through this method, the relationship between the EEG components and the acoustic features could be measured. Moreover, according to the temporal relations, an optimal offset of acoustic features was found, which could obtain better alignment with EEG features. After the regression analysis, the significant EEG components were found, which were in good agreement with cognitive laws. This provides a new idea for long-term EEG components, which could be applied in other correlative subjects.

scholarly journals Decoding the covert shift of spatial attention from electroencephalographic signals permits reliable control of a brain-computer interface

2020 ◽  
Author(s):  
Christoph Reichert ◽  
Stefan Dürschmid ◽  
Mandy V. Bartsch ◽  
Jens-Max Hopf ◽  
Hans-Jochen Heinze ◽  
...  
Keyword(s):  
Event Related Potentials ◽  
Right Visual Field ◽  
Spatial Filters ◽  
Computer Interfaces ◽  
Eeg Data ◽  
Related Potentials ◽  
Left And Right ◽  
Viable Approach ◽  
Number Of Classes ◽  
Electroencephalogram Eeg

AbstractObjectiveOne of the main goals of brain-computer interfaces (BCI) is to restore communication abilities in patients. BCIs often use event-related potentials (ERPs) like the P300 which signals the presence of a target in a stream of stimuli. The P300 and related approaches, however, are inherently limited, as they require many stimulus presentations to obtain a usable control signal. Many approaches depend on gaze-direction to focus the target, which is also not a viable approach in many cases, because eye movements might be impaired in potential users. Here we report on a BCI that avoids both shortcomings by decoding spatial target information, independent of gaze shifts.ApproachWe present a new method to decode from the electroencephalogram (EEG) covert shifts of attention to one out of four targets simultaneously presented in the left and right visual field. The task is designed to evoke the N2pc component – a hemisphere lateralized response, elicited over the occipital scalp contralateral to the attended target. The decoding approach involves decoding of the N2pc based on data-driven estimation of spatial filters and a correlation measure.Main resultsDespite variability of decoding performance across subjects, 22 out of 24 subjects performed well above chance level. Six subjects even exceeded 80% (cross-validated: 89%) correct predictions in a four-class discrimination task. Hence, the single-trial N2pc proves to be a component that allows for reliable BCI control. An offline analysis of the EEG data with respect to their dependence on stimulation time and number of classes demonstrates that the present method is also a workable approach for two-class tasks.SignificanceOur method extends the range of strategies for gaze-independent BCI control. The proposed decoding approach has the potential to be efficient in similar applications intended to decode ERPs.

scholarly journals Application of Deep Learning for Emotion Recognition

YMER Digital ◽  
2021 ◽  
Vol 20 (12) ◽  
pp. 834-840
Author(s):  
Varsha R Toshniwal ◽  
◽  
Pooja S Puri ◽  
Keyword(s):  
Network Architecture ◽  
Emotional State ◽  
Human Perception ◽  
Brain Regions ◽  
Eeg Signals ◽  
Neural Network Architecture ◽  
Computer Interfaces ◽  
Alpha Beta ◽  
Electroencephalogram Eeg ◽  
The Brain

The electroencephalogram (EEG) gained a lot of importance in recent years because of its property to depict the nature and actions of human perception. EEG signals are good at capturing the emotional state of a person by measuring the neuronal activities in different regions of the brain. Lots of EEG-based brain-computer interfaces with a different number of channels ( 62 channels, 32 channels, etc.) are being used to capture neuronal activities which can be segmented into different frequency ranges (delta, theta, alpha. beta and gamma). This paper puts forward a neural network architecture for the recognition of emotion from EEG signals and a study providing the set of brain regions and the frequency type associated with the corresponding brain region which contributes most for the detection of emotion though EEG signals. For experimentation, SEED-IV dataset has been used

scholarly journals Selective Subject Pooling Strategy to Improve Model Generalization for a Motor Imagery BCI

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5436
Author(s):  
Kyungho Won ◽  
Moonyoung Kwon ◽  
Minkyu Ahn ◽  
Sung Chan Jun
Keyword(s):  
Motor Imagery ◽  
Comparative Performance ◽  
Computer Interfaces ◽  
Model Generalization ◽  
Improve Model ◽  
Eeg Data ◽  
Subject Variability ◽  
Pooling Strategies ◽  
Pooling Strategy ◽  
Electroencephalogram Eeg

Brain–computer interfaces (BCIs) facilitate communication for people who cannot move their own body. A BCI system requires a lengthy calibration phase to produce a reasonable classifier. To reduce the duration of the calibration phase, it is natural to attempt to create a subject-independent classifier with all subject datasets that are available; however, electroencephalogram (EEG) data have notable inter-subject variability. Thus, it is very challenging to achieve subject-independent BCI performance comparable to subject-specific BCI performance. In this study, we investigate the potential for achieving better subject-independent motor imagery BCI performance by conducting comparative performance tests with several selective subject pooling strategies (i.e., choosing subjects who yield reasonable performance selectively and using them for training) rather than using all subjects available. We observed that the selective subject pooling strategy worked reasonably well with public MI BCI datasets. Finally, based upon the findings, criteria to select subjects for subject-independent BCIs are proposed here.

The Relation Between Aerobic Fitness and Cognitive Performance

2016 ◽  
Vol 30 (3) ◽  
pp. 102-113 ◽  
Author(s):  
Chun-Hao Wang ◽  
Chun-Ming Shih ◽  
Chia-Liang Tsai
Keyword(s):  
Cognitive Performance ◽  
Aerobic Fitness ◽  
Reaction Times ◽  
Mediation Model ◽  
Brain Potentials ◽  
Fitness Level ◽  
Eeg Data ◽  
Attention Performance ◽  
P3 Amplitude ◽  
The Relationship

Abstract. This study aimed to assess whether brain potentials have significant influences on the relationship between aerobic fitness and cognition. Behavioral and electroencephalographic (EEG) data was collected from 48 young adults when performing a Posner task. Higher aerobic fitness is related to faster reaction times (RTs) along with greater P3 amplitude and shorter P3 latency in the valid trials, after controlling for age and body mass index. Moreover, RTs were selectively related to P3 amplitude rather than P3 latency. Specifically, the bootstrap-based mediation model indicates that P3 amplitude mediates the relationship between fitness level and attention performance. Possible explanations regarding the relationships among aerobic fitness, cognitive performance, and brain potentials are discussed.

Large-sample confidence intervals of information-theoretic measures in linguistics

2020 ◽  
Vol 6 (1) ◽  
pp. 19-54
Author(s):  
Ryan Ka Yau Lai ◽  
Youngah Do
Keyword(s):  
Maximum Likelihood ◽  
Corpus Linguistics ◽  
Delta Method ◽  
Confidence Bounds ◽  
Likelihood Estimator ◽  
Information Theoretic ◽  
Leibler Divergence ◽  
Information Theoretic Measures ◽  
Data Points ◽  
Measure Of Uncertainty

This article explores a method of creating confidence bounds for information-theoretic measures in linguistics, such as entropy, Kullback-Leibler Divergence (KLD), and mutual information. We show that a useful measure of uncertainty can be derived from simple statistical principles, namely the asymptotic distribution of the maximum likelihood estimator (MLE) and the delta method. Three case studies from phonology and corpus linguistics are used to demonstrate how to apply it and examine its robustness against common violations of its assumptions in linguistics, such as insufficient sample size and non-independence of data points.

PERSOALAN KEHIDUPAN KONTEMPORER: MENGGAGAS KAJIAN SACHEDINA TENTANG THEOLOGI PLURALISME

EMPIRISMA ◽  
2017 ◽  
Vol 26 (1) ◽  
Author(s):  
Limas Dodi
Keyword(s):  
Religious Pluralism ◽  
Main Argument ◽  
Religious Groups ◽  
Common View ◽  
The Public ◽  
The People ◽  
Muslim Majority ◽  
The Common ◽  
The Relationship ◽  
Religious Differences

According to Abdulaziz Sachedina, the main argument of religious pluralism in the Qur’an based on the relationship between private belief (personal) and public projection of Islam in society. By regarding to private faith, the Qur’an being noninterventionist (for example, all forms of human authority should not be disturb the inner beliefs of individuals). While the public projection of faith, the Qur’an attitude based on the principle of coexistence. There is the willingness of the dominant race provide the freedom for people of other faiths with their own rules. Rules could shape how to run their affairs and to live side by side with the Muslims. Thus, based on the principle that the people of Indonesia are Muslim majority, it should be a mirror of a societie’s recognizion, respects and execution of religious pluralism. Abdul Aziz Sachedina called for Muslims to rediscover the moral concerns of public Islam in peace. The call for peace seemed to indicate that the existence of increasingly weakened in the religious sense of the Muslims and hence need to be reaffi rmed. Sachedina also like to emphasize that the position of peace in Islam is parallel with a variety of other doctrines, such as: prayer, fasting, pilgrimage and so on. Sachedina also tried to show the argument that the common view among religious groups is only one religion and traditions of other false and worthless. “Antipluralist” argument comes amid the reality of human religious differences. Keywords: Theology, Pluralism, Abdulaziz Sachedina

Migraine: Genetic Variants and Clinical Phenotypes

2019 ◽  
Vol 26 (34) ◽  
pp. 6207-6221 ◽  
Author(s):  
Innocenzo Rainero ◽  
Alessandro Vacca ◽  
Flora Govone ◽  
Annalisa Gai ◽  
Lorenzo Pinessi ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Association Studies ◽  
Mthfr Gene ◽  
Genome Wide Association Studies ◽  
Clinical Phenotypes ◽  
Network Analyses ◽  
Genome Wide ◽  
Genomic Studies ◽  
The Common ◽  
The Relationship

Migraine is a common, chronic neurovascular disorder caused by a complex interaction between genetic and environmental risk factors. In the last two decades, molecular genetics of migraine have been intensively investigated. In a few cases, migraine is transmitted as a monogenic disorder, and the disease phenotype cosegregates with mutations in different genes like CACNA1A, ATP1A2, SCN1A, KCNK18, and NOTCH3. In the common forms of migraine, candidate genes as well as genome-wide association studies have shown that a large number of genetic variants may increase the risk of developing migraine. At present, few studies investigated the genotype-phenotype correlation in patients with migraine. The purpose of this review was to discuss recent studies investigating the relationship between different genetic variants and the clinical characteristics of migraine. Analysis of genotype-phenotype correlations in migraineurs is complicated by several confounding factors and, to date, only polymorphisms of the MTHFR gene have been shown to have an effect on migraine phenotype. Additional genomic studies and network analyses are needed to clarify the complex pathways underlying migraine and its clinical phenotypes.

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