scholarly journals A Novel Processing Model for P300 Brainwaves Detection

2021 ◽  
Author(s):  
Wanus Srimaharaj ◽  
Roungsan Chaisricharoen
Keyword(s):  
Signal Processing ◽  
Evoked Potential ◽  
Brain Activity ◽  
Original Data ◽  
Event Related Potential ◽  
Processing Application ◽  
Feature Extraction Method ◽  
Channel Classification ◽  
Novel Processing ◽  
The Individual

Event-related potential (ERP) is a distinctive pattern of brain activity that is elicited by the brain’s sensitivity and cognition whereas P300 evoked potential changes in cognitive functions. Since P300 wave is a cognitive response across multiple brain channels correlated between the measured electroencephalogram (EEG) and deviant stimulus in a specific period, it requires a suitable signal processing application for interpretation. Moreover, multiple steps of data processing under neuroscience criteria make the P300 reflection difficult to analyze by common methods. Therefore, this study proposes the processing model for brainwave applications based on P300 peak signal detection in multiple brain channels. This study applies 64 channels ERP datasets throughout bandpass filter in fast Fourier transform (FFT) with the specific ranges of signal processing while ERP averaging is applied as a feature extraction method. Furthermore, the experimental metadata is applied with the filtered P300 peak signals in channel classification via a machine learning method, the Decision Tree. The experimental results indicate the accurate mental reflection of P300 evoked potential in different brain channels with high classification accuracy relying on the contrast condition throughout the original data source averaged across the individual electrodes.

scholarly journals The Neural Correlates of Conflict Detection and Resolution During Multiword Lexical Selection: Evidence from Bilinguals and Monolinguals

2019 ◽  
Vol 9 (5) ◽  
pp. 110 ◽  
Author(s):  
Manuel F. Pulido ◽  
Paola E. Dussias
Keyword(s):  
Native Speakers ◽  
Brain Activity ◽  
Native English Speakers ◽  
Conflict Detection ◽  
Event Related Potential ◽  
English Speakers ◽  
Lexical Selection ◽  
Multiword Units ◽  
Conflict Detection And Resolution ◽  
The Individual

Previous studies have identified the Event Related Potential (ERP) components of conflict detection and resolution mechanisms in tasks requiring lexical selection at the individual word level. We investigated the brain potentials associated with these mechanisms in a lexical selection task based on multiword units made up of verb–noun combinations (e.g., eat breakfast, skip school). Native and non-native English speakers were asked to select a familiarized target verb–noun sequence (eat breakfast) between two choices. Trials were low-conflict, with only one plausible candidate (e.g., eat – shoot – breakfast) or high-conflict, with two plausible verbs (e.g., eat – skip – breakfast). Following the presentation of the noun, native English speakers showed a biphasic process of selection, with a conflict-detection centro-parietal negativity between 500 and 600 ms (Ninc), followed by a right frontal effect (RFE) between 600 and 800 ms preceding responses. Late Spanish–English bilinguals showed a similar but more sustained and more widespread effect. Additionally, brain activity was only significantly correlated with performance in native speakers. Results suggest largely similar basic mechanisms, but also that different resources and strategies are engaged by non-native speakers when resolving conflict in the weaker language, with a greater focus on individual words than on multiword units.

scholarly journals EEG-based Computer Control Interface for Brain-Machine Interaction

2015 ◽  
Vol 11 (6) ◽  
pp. 43 ◽  
Author(s):  
Jozsef Katona ◽  
A. Kovari
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Brain Activity ◽  
Computer Control ◽  
Digital Signal ◽  
Control Methods ◽  
Processing Application ◽  
Control Applications ◽  
Control Interface ◽  
Machine Control

Recently more and more research methods are available to observe brain activity; for instance, Functional Magnetic Resonance Imaging (fMRI), Positron Emission Tomography (PET), Transcranial Magnetic Stimulation (TMS), Near Infrared Spectroscopy (NIRS), Electroencephalograph (EEG) or Magnetoencephalography (MEG), which provide new research opportunities for several applications. For example, control methods based on the evaluation of measurable signals of human brain activity. In the past few years, more mobile EEG (electroencephalogram) based brain activity biosensor and signal processing devices have become available not only for medical examinations, but also to be used in different scopes; for instance, in control applications. These methods provide completely new possibilities in human-machine interactions by digital signal processing of brain signals. In this study, the program model, the establishment, the implementation and the test results of the quantitative EEG-based computer control interface, protocol and digital signal processing application are demonstrated. The user-friendly visualization of the evaluated brain wave signals is implemented in visual C# object-oriented language. This EEG-based control unit and interface provides an adequate basis for further research in different fields of brain-machine control methods regarding the examination of possible machine control applications.

Seeing Sounds and Hearing Colors: An Event-related Potential Study of Auditory–Visual Synesthesia

2009 ◽  
Vol 21 (10) ◽  
pp. 1869-1881 ◽  
Author(s):  
Aviva I. Goller ◽  
Leun J. Otten ◽  
Jamie Ward
Keyword(s):  
Evoked Potential ◽  
Brain Activity ◽  
Visual Illusions ◽  
Event Related Potential ◽  
Auditory Evoked Potential ◽  
Auditory Target ◽  
Electrical Brain Activity ◽  
And Control ◽  
Brief Tones ◽  
The Brain

In auditory–visual synesthesia, sounds automatically elicit conscious and reliable visual experiences. It is presently unknown whether this reflects early or late processes in the brain. It is also unknown whether adult audiovisual synesthesia resembles auditory-induced visual illusions that can sometimes occur in the general population or whether it resembles the electrophysiological deflection over occipital sites that has been noted in infancy and has been likened to synesthesia. Electrical brain activity was recorded from adult synesthetes and control participants who were played brief tones and required to monitor for an infrequent auditory target. The synesthetes were instructed to attend either to the auditory or to the visual (i.e., synesthetic) dimension of the tone, whereas the controls attended to the auditory dimension alone. There were clear differences between synesthetes and controls that emerged early (100 msec after tone onset). These differences tended to lie in deflections of the auditory-evoked potential (e.g., the auditory N1, P2, and N2) rather than the presence of an additional posterior deflection. The differences occurred irrespective of what the synesthetes attended to (although attention had a late effect). The results suggest that differences between synesthetes and others occur early in time, and that synesthesia is qualitatively different from similar effects found in infants and certain auditory-induced visual illusions in adults. In addition, we report two novel cases of synesthesia in which colors elicit sounds, and vice versa.

scholarly journals Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5309
Author(s):  
Akira Ikeda ◽  
Yoshikazu Washizawa
Keyword(s):  
Steady State ◽  
Information Transfer ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Extraction Methods ◽  
Stimulation Frequency ◽  
Event Related Potential ◽  
Spectrum Estimation ◽  
Complex Valued ◽  
Visual Evoked

The steady-state visual evoked potential (SSVEP), which is a kind of event-related potential in electroencephalograms (EEGs), has been applied to brain–computer interfaces (BCIs). SSVEP-based BCIs currently perform the best in terms of information transfer rate (ITR) among various BCI implementation methods. Canonical component analysis (CCA) or spectrum estimation, such as the Fourier transform, and their extensions have been used to extract features of SSVEPs. However, these signal extraction methods have a limitation in the available stimulation frequency; thus, the number of commands is limited. In this paper, we propose a complex valued convolutional neural network (CVCNN) to overcome the limitation of SSVEP-based BCIs. The experimental results demonstrate that the proposed method overcomes the limitation of the stimulation frequency, and it outperforms conventional SSVEP feature extraction methods.

scholarly journals Speech Identification with Temporal and Spectral Modification in Subjects with Auditory Neuropathy

2012 ◽  
Vol 2012 ◽  
pp. 1-7
Author(s):  
Vijaya Kumar Name ◽  
C. S. Vanaja
Keyword(s):  
Signal Processing ◽  
Speech Processing ◽  
Word Identification ◽  
Low Frequency ◽  
Auditory Neuropathy ◽  
Study Word ◽  
Spectral Modification ◽  
The Individual ◽  
High Pass ◽  
Speech Identification

Background. The aim of this study was to investigate the individual effects of envelope enhancement and high-pass filtering (500 Hz) on word identification scores in quiet for individuals with Auditory Neuropathy. Method. Twelve individuals with Auditory Neuropathy (six males and six females) with ages ranging from 12 to 40 years participated in the study. Word identification was assessed using bi-syllabic words in each of three speech processing conditions: unprocessed, envelope-enhanced, and high-pass filtered. All signal processing was carried out using MATLAB-7. Results. Word identification scores showed a mean improvement of 18% with envelope enhanced versus unprocessed speech. No significant improvement was observed with high-pass filtered versus unprocessed speech. Conclusion. These results suggest that the compression/expansion signal processing strategy enhances speech identification scores—at least for mild and moderately impaired individuals with AN. In contrast, simple high-pass filtering (i.e., eliminating the low-frequency content of the signal) does not improve speech perception in quiet for individuals with Auditory Neuropathy.

Off-line classification of Polish vowel spectra using artificial neural networks

2004 ◽  
Vol 34 (1) ◽  
pp. 37-52
Author(s):  
Wiktor Jassem ◽  
Waldemar Grygiel
Keyword(s):  
Neural Networks ◽  
Numerical Data ◽  
Word List ◽  
Original Data ◽  
Data Matrix ◽  
Probabilistic Neural Networks ◽  
Independent Variable ◽  
The Individual ◽  
Vowel Phoneme

The mid-frequencies and bandwidths of formants 1–5 were measured at targets, at plus 0.01 s and at minus 0.01 s off the targets of vowels in a 100-word list read by five male and five female speakers, for a total of 3390 10-variable spectrum specifications. Each of the six Polish vowel phonemes was represented approximately the same number of times. The 3390* 10 original-data matrix was processed by probabilistic neural networks to produce a classification of the spectra with respect to (a) vowel phoneme, (b) identity of the speaker, and (c) speaker gender. For (a) and (b), networks with added input information from another independent variable were also used, as well as matrices of the numerical data appropriately normalized. Mean scores for classification with respect to phonemes in a multi-speaker design in the testing sets were around 95%, and mean speaker-dependent scores for the phonemes varied between 86% and 100%, with two speakers scoring 100% correct. The individual voices were identified between 95% and 96% of the time, and classifications of the spectra for speaker gender were practically 100% correct.

PINPOINT SOURCE LOCALIZATION FOR OCULAR NONSELECTIVE ATTENTION WITH COMBINATION OF ERP AND fNIRI MEASUREMENTS

2008 ◽  
Vol 01 (02) ◽  
pp. 195-206 ◽  
Author(s):  
TING LI ◽  
LI LI ◽  
PENG DU ◽  
QINGMING LUO ◽  
HUI GONG
Keyword(s):  
Near Infrared ◽  
Infrared Imaging ◽  
Brain Activity ◽  
Event Related Potential ◽  
Reliable Technique ◽  
Brain Mechanism ◽  
Electrophysiological Signals ◽  
Series Of Experiments ◽  
A New Technique ◽  
Hemodynamic Information

Compared with event-related potential (ERP) which is widely used in psychology research, functional near-infrared imaging (fNIRI) is a new technique providing hemodynamic information related to brain activity, except for electrophysiological signals. Here, we use both these techniques to study ocular attention. We conducted a series of experiments with a classic paradigm of ocular nonselective attention, and monitored responses with fNIRI and ERP respectively. The results showed that fNIRI measured brain activations in the left prefrontal lobe, while ERPs showed activation in frontal lobe. More importantly, only with the combination measurements of fNIRI and ERP, we were then able to find the pinpoint source of ocular nonselective attention, which is in the left and upper corner in Brodmann area 10. These results demonstrated that fNIRI is a reliable technique in psychology, and the combination of fNIRI and ERP can be promising to reveal more information in the research of brain mechanism.

scholarly journals ERP CORE: An Open Resource for Human Event-Related Potential Research

2020 ◽  
Author(s):  
Emily S. Kappenman ◽  
Jaclyn Farrens ◽  
Wendy Zhang ◽  
Andrew X Stewart ◽  
Steven J Luck
Keyword(s):  
Data Processing ◽  
Brain Activity ◽  
A Priori ◽  
Event Related Potentials ◽  
Event Related Potential ◽  
Error Related Negativity ◽  
Wide Range ◽  
Experiment Control ◽  
Related Potentials

Event-related potentials (ERPs) are noninvasive measures of human brain activity that index a range of sensory, cognitive, affective, and motor processes. Despite their broad application across basic and clinical research, there is little standardization of ERP paradigms and analysis protocols across studies. To address this, we created ERP CORE (Compendium of Open Resources and Experiments), a set of optimized paradigms, experiment control scripts, data processing pipelines, and sample data (N = 40 neurotypical young adults) for seven widely used ERP components: N170, mismatch negativity (MMN), N2pc, N400, P3, lateralized readiness potential (LRP), and error-related negativity (ERN). This resource makes it possible for researchers to 1) employ standardized ERP paradigms in their research, 2) apply carefully designed analysis pipelines and use a priori selected parameters for data processing, 3) rigorously assess the quality of their data, and 4) test new analytic techniques with standardized data from a wide range of paradigms.

scholarly journals The Localization of Activity Sources in a Three-Dimensional Model of the Human Brain Using the Joint Analysis of EEG / sMRI Data

2019 ◽  
Vol 17 (3) ◽  
pp. 18-28
Author(s):  
E. Bykova ◽  
A. Savostyanov
Keyword(s):  
Inverse Problem ◽  
Brain Activity ◽  
Three Dimensional ◽  
Human Head ◽  
Joint Analysis ◽  
Dimensional Model ◽  
Three Dimensional Model ◽  
Use Of Data ◽  
The Individual ◽  
The Brain

Despite the large number of existing methods of the diagnosis of the brain, brain remains the least studied part of the human body. Electroencephalography (EEG) is one of the most popular methods of studying of brain activity due to its relative cheapness, harmless, and mobility of equipment. While analyzing the EEG data of the brain, the problem of solving of the inverse problem of electroencephalography, the localization of the sources of electrical activity of the brain, arises. This problem can be formulated as follows: according to the signals recorded on the surface of the head, it is necessary to determine the location of sources of these signals in the brain. The purpose of my research is to develop a software system for localization of brain activity sources based on the joint analysis of EEG and sMRI data. There are various approaches to solving of the inverse problem of EEG. To obtain the most exact results, some of them involve the use of data on the individual anatomy of the human head – structural magnetic resonance imaging (sMRI data). In this paper, one of these approaches is supposed to be used – Electromagnetic Spatiotemporal Independent Component Analysis (EMSICA) proposed by A. Tsai. The article describes the main stages of the system, such as preprocessing of the initial data; the calculation of the special matrix of the EMSICA approach, the values of which show the level of activity of a certain part of the brain; visualization of brain activity sources on its three-dimensional model.

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