A New Spatiotemporal Filtering Method for Single-Trial Estimation of Correlated ERP Subcomponents

Jarchi, Delaram; Sanei, Saeid; Prı́ncipe, José C.; Makkiabadi, Bahador

doi:10.1109/tbme.2010.2083660

Cited by 33 publications

(18 citation statements)

References 24 publications

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“…Basically, the computation of ERPs requires averaging EEG signals over multiple trials, rendering ERP features inappropriate for online computing. However, recent developments of the single-trial ERP computation methods increase a possibility to use ERP features for online emotional state estimation [42–46]. …”

Section: Eeg Correlates Of Emotionmentioning

confidence: 99%

A Review on the Computational Methods for Emotional State Estimation from the Human EEG

Kim

et al. 2013

Computational and Mathematical Methods in Medicine

234

150

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A growing number of affective computing researches recently developed a computer system that can recognize an emotional state of the human user to establish affective human-computer interactions. Various measures have been used to estimate emotional states, including self-report, startle response, behavioral response, autonomic measurement, and neurophysiologic measurement. Among them, inferring emotional states from electroencephalography (EEG) has received considerable attention as EEG could directly reflect emotional states with relatively low costs and simplicity. Yet, EEG-based emotional state estimation requires well-designed computational methods to extract information from complex and noisy multichannel EEG data. In this paper, we review the computational methods that have been developed to deduct EEG indices of emotion, to extract emotion-related features, or to classify EEG signals into one of many emotional states. We also propose using sequential Bayesian inference to estimate the continuous emotional state in real time. We present current challenges for building an EEG-based emotion recognition system and suggest some future directions.

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Section: Eeg Correlates Of Emotionmentioning

confidence: 99%

A Review on the Computational Methods for Emotional State Estimation from the Human EEG

Kim

et al. 2013

Computational and Mathematical Methods in Medicine

234

150

View full text Add to dashboard Cite

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“…This algorithm overcome one subcomponent within the spatial domain at the same time as diagnosing the other one within the temporal domain. [5] If a person wants to move any of his body parts, several changes will be observed in brain. The motor cortex present inside the brain will increase its activity to pass the signal to execute an action.…”

Section: Fig 2 Eeg Based Bci Systemmentioning

confidence: 99%

Electroencephalogram based Brain Computer Interface System Analysis

2020

IJITEE

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Electroencephalogram is the study of electrical signals of the brain recorded using a mesh like structure containing electrodes that is placed on the scalp. The history of EEG dates more than a century back, when the brains of rabbits and monkeys were studied and almost 50 years later in the year 1924, the first ever brain activity of human was noted by famed psychiatrist and physiologist Hans Berger from Germany. EEG based systems that can communicate with the brain are categorized as Brain Computer Interface i.e. BCI. The electrodes read the brain signals, amplifies them in order to be studied more accurately by the machine send them to machine after converting it into digital form. With the recent changes in technology better electrodes are being used which can catch highly sensitive signals as well.[1][2] EEG based BCI systems can change the world for many people as it holds so much power if thought properly. This paper reviews the EEG functioning and some innovations in BCI and also proposes ideas about potential help for people suffering from hearing and speech impairment.

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“…However, most works assume their trials are aligned and have a reduced search space for τ (e.g. [2], [4], [7]). This can be too restrictive an assumption if the goal is to build a detector or for validation purposes.…”

Section: Related Workmentioning

confidence: 99%

“…There is an extensive literature on single-trial analysis, refer to [2] for a review. This type of analysis has applications in the study of certain conditions, such as schizophrenia [3] or fatigue [2]. The detection of single-trial ERPs can also be used in brain-computer-interfaces [4].…”

Section: Introductionmentioning

confidence: 99%

Bayesian detection of single-trial event-related potentials

Mestre

Godsill

Fitzgerald

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The goal of this paper is to build a detector of event-related potentials (ERP) in single-trial EEG data. This problem can be reformulated as a parameter estimation problem, where the parameter of interest is the time of occurrence of the ERP. This type of detector has clinical applications (study of schizophrenia, fatigue), or applications in brain-computer-interfaces. However, the poor signal-to-noise ratio (SNR) and lack of understanding of the noise generating process make this a challenging task. In this paper, we take a Bayesian approach, samples are drawn from the posterior of the parameter of interest using Markov chain Monte Carlo (MCMC). Different noise covariances from Gaussian processes are tested. We show that it is possible to pick up the ERP signal in spite of the poor SNR with an appropriate choice of noise covariance structure.

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A New Spatiotemporal Filtering Method for Single-Trial Estimation of Correlated ERP Subcomponents

Cited by 33 publications

References 24 publications

A Review on the Computational Methods for Emotional State Estimation from the Human EEG

A Review on the Computational Methods for Emotional State Estimation from the Human EEG

Electroencephalogram based Brain Computer Interface System Analysis

Bayesian detection of single-trial event-related potentials

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