EEG Analysis of Imagined Speech

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.

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High-Frequency Oscillations in the Scalp Electroencephalogram: Mission Impossible without Computational Intelligence

Computational Intelligence and Neuroscience ◽

10.1155/2018/1638097 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 6

Author(s):

Peter Höller ◽

Eugen Trinka ◽

Yvonne Höller

Keyword(s):

High Frequency ◽

Visual Detection ◽

Signal To Noise Ratio ◽

Physiological Activity ◽

Automated Detection ◽

Detection Algorithms ◽

Scalp Eeg ◽

High Frequency Oscillations ◽

Scalp Electroencephalogram ◽

Electroencephalogram Eeg

High-frequency oscillations (HFOs) in the electroencephalogram (EEG) are thought to be a promising marker for epileptogenicity. A number of automated detection algorithms have been developed for reliable analysis of invasively recorded HFOs. However, invasive recordings are not widely applicable since they bear risks and costs, and the harm of the surgical intervention of implantation needs to be weighted against the informational benefits of the invasive examination. In contrast, scalp EEG is widely available at low costs and does not bear any risks. However, the detection of HFOs on the scalp represents a challenge that was taken on so far mostly via visual detection. Visual detection of HFOs is, in turn, highly time-consuming and subjective. In this review, we discuss that automated detection algorithms for detection of HFOs on the scalp are highly warranted because the available algorithms were all developed for invasively recorded EEG and do not perform satisfactorily in scalp EEG because of the low signal-to-noise ratio and numerous artefacts as well as physiological activity that obscures the tiny phenomena in the high-frequency range.

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SYNCHRONIZATION MEASURES OF THE SCALP ELECTROENCEPHALOGRAM CAN DISCRIMINATE HEALTHY FROM ALZHEIMER'S SUBJECTS

International Journal of Neural Systems ◽

10.1142/s0129065707000932 ◽

2007 ◽

Vol 17 (02) ◽

pp. 61-69 ◽

Cited By ~ 48

Author(s):

MARK A. KRAMER ◽

FEN-LEI CHANG ◽

MAURICE E. COHEN ◽

DONNA HUDSON ◽

ANDREW J. SZERI

Keyword(s):

Cognitive Impairment ◽

Healthy Subjects ◽

Phase Synchronization ◽

Eyes Closed ◽

Eeg Data ◽

Two Measures ◽

Left And Right ◽

Scalp Electroencephalogram ◽

Occipital Lobes ◽

Electroencephalogram Eeg

Three synchronization measures are applied to scalp electroencephalogram (EEG) data collected from 20 patients diagnosed to have either: (1) no dementia, (2) mild cognitive impairment (MCI), or (3) Alzheimer's disease (AD). We apply the three synchronization measures — the phase synchronization, and two measures of nonlinear interdependency — to the data collected from awake patients resting with eyes closed. We show that the synchronization in potential between electrodes near the left and right occipital lobes provides a statistically significant discriminant between the healthy and AD subjects, and the MCI and AD subjects. None of the three measures appears able to distinguish between the healthy and MCI subjects, although MCI subjects show synchronization values intermediate between healthy subjects (with high synchronization values) and AD subjects (with low synchronization values) on average.

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Empirical Mode Decomposition and its Extensions Applied to EEG Analysis: A Review

Advances in Data Science and Adaptive Analysis ◽

10.1142/s2424922x18400016 ◽

2018 ◽

Vol 10 (02) ◽

pp. 1840001 ◽

Cited By ~ 6

Author(s):

Catherine M. Sweeney-Reed ◽

Slawomir J. Nasuto ◽

Marcus F. Vieira ◽

Adriano O. Andrade

Keyword(s):

Empirical Mode Decomposition ◽

Data Driven ◽

Eeg Analysis ◽

Time Frequency ◽

Computer Interfaces ◽

Mode Decomposition ◽

Eeg Data ◽

Wide Range ◽

Challenges And Opportunities ◽

Data Driven Approach

Empirical mode decomposition (EMD) provides an adaptive, data-driven approach to time–frequency analysis, yielding components from which local amplitude, phase, and frequency content can be derived. Since its initial introduction to electroencephalographic (EEG) data analysis, EMD has been extended to enable phase synchrony analysis and multivariate data processing. EMD has been integrated into a wide range of applications, with emphasis on denoising and classification. We review the methodological developments, providing an overview of the diverse implementations, ranging from artifact removal to seizure detection and brain–computer interfaces. Finally, we discuss limitations, challenges, and opportunities associated with EMD for EEG analysis.

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

Entropy ◽

10.3390/e20010007 ◽

2018 ◽

Vol 20 (1) ◽

pp. 7 ◽

Cited By ~ 11

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.

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Current Practices in Electroencephalogram- Based Brain-Computer Interfaces

Encyclopedia of Information Science and Technology, Second Edition ◽

10.4018/978-1-60566-026-4.ch143 ◽

2011 ◽

pp. 888-901 ◽

Cited By ~ 5

Author(s):

Ramaswamy Palaniappan ◽

Chanan S. Syan ◽

Raveendran Paramesran

Keyword(s):

Electrical Activity ◽

Communication Systems ◽

External Environment ◽

Assistive Technologies ◽

Memory Impairments ◽

Menu Selection ◽

Scalp Eeg ◽

Computer Interfaces ◽

Electroencephalogram Eeg ◽

The Brain

Electroencephalogram (EEG) is the electrical activity of the brain recorded by electrodes placed on the scalp. EEG signals are generally investigated for the diagnosis of mental conditions such as epilepsy, memory impairments, and sleep disorders. In recent years there has been another application using EEG: for brain-computer interface (BCI) designs (Vaughan & Wolpaw, 2006). EEG-based BCI designs are very useful for hands-off device control and communication as they use the electrical activity of the brain to interface with the external environment, therefore circumventing the use of peripheral muscles and limbs. Some current applications of BCIs in communication systems are for paralyzed individuals to communicate with their surroundings through character/menu selection and in device control such as wheelchair movement, prosthetics control, and flight and rehabilitative (assistive) technologies. For the general public, some of the possible applications are hands-off menu selection, flight/space control, and virtual reality (entertainment). BCI has also been applied in biometrics (Palaniappan & Mandic, 2007).

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Model-Based Analysis and Optimization of the Mapping of Cortical Sources in the Spontaneous Scalp EEG

Computational and Mathematical Methods in Medicine ◽

10.1080/17486700701526289 ◽

2007 ◽

Vol 8 (3) ◽

pp. 173-189 ◽

Cited By ~ 2

Author(s):

Andrei V. Sazonov ◽

Jan W. M. Bergmans ◽

Pierre J. M. Cluitmans ◽

Paul A. M. Griep ◽

Johan B. A. M. Arends ◽

...

Keyword(s):

Generation Model ◽

Volume Conduction ◽

Channel Correlation ◽

Scalp Eeg ◽

Observation Function ◽

Scalp Electroencephalogram ◽

The Impact ◽

Electroencephalogram Eeg ◽

Model Based Analysis ◽

Conduction Properties

The mapping of brain sources into the scalp electroencephalogram (EEG) depends on volume conduction properties of the head and on an electrode montage involving a reference. Mathematically, thissource mapping(SM) is fully determined by anobservation function(OF) matrix. This paper analyses the OF-matrix for a generation model for the desynchronized spontaneous EEG. The model involves a four-shell spherical volume conductor containing dipolar sources that are mutually uncorrelated so as to reflect the desynchronized EEG. The reference is optimized in order to minimize the impact in the SM of the sources located distant from the electrodes. The resulting reference is called thelocalized reference(LR). The OF-matrix is analyzed in terms of the relative power contribution of the sources and the cross-channel correlation coefficient for five existing references as well as for the LR. It is found that theHjorth Laplacian referenceis a fair approximation of the LR, and thus is close to optimum for practical intents and purposes. The other references have a significantly poorer performance. Furthermore, the OF-matrix is analyzed for limits to the spatial resolution for the EEG. These are estimated to be around 2 cm.

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

Sensors ◽

10.3390/s21165436 ◽

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.

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

10.1101/2020.07.29.226456 ◽

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.

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Quantitative Measurement of Brain Electrical Activity in Delirium

International Psychogeriatrics ◽

10.1017/s1041610291000698 ◽

1991 ◽

Vol 3 (2) ◽

pp. 231-247 ◽

Cited By ~ 11

Author(s):

Andrew F. Leuchter ◽

Sandra A. Jacobson

Keyword(s):

Visual Inspection ◽

Quantitative Methods ◽

Quantitative Measurement ◽

Quantitative Eeg ◽

Eeg Analysis ◽

Eeg Data ◽

Eeg Power ◽

Logistical Problem ◽

Work Done ◽

Electroencephalogram Eeg

It has long been known that the conventional electroencephalogram (EEG) is a useful tool in the evaluation of delirium. There are moderate correlations between the amount of slowing seen on EEG and the degree of confusion or level of arousal observed among delirious patients. The usefulness of the EEG for assessment and diagnosis in this area has been limited, however, by: (a) difficulties in assessing the significance of slow-wave activity, (b) problems in detecting changes in relative EEG power, and (c) the logistical problem of lengthy recording sessions with agitated patients. This article selectively reviews the development of quantitative EEG methods and presents preliminary data from an ongoing longitudinal study of the use of these methods among elderly delirious patients. There has been little work done in applying quantitative methods to the study of delirium, and no work done that has systematically compared conventional EEG analysis to quantitative analysis. Delirium shares electrophysiological characteristics with other organic mental syndromes, however, where quantitative EEG has been shown to be useful. Furthermore, analysis of digital EEG data is inherently superior to visual inspection in assessing the distribution of EEG power among different frequency bands. Previous studies, as well as data presented here, suggest that quantitative EEG is a clinically useful supplement to the conventional EEG for the assessment of elderly patients with delirium.

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