Classification of Left/Right Hand Movement EEG Signals Using Event Related Potentials and Advanced Features

Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device bypassing conventional motor output pathways of nerves and muscles. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. With respect to the topographic patterns of brain rhythm modulations, the common spatial patterns (CSPs) algorithm has been proven to be very useful to produce subject-specific and discriminative spatial filters; but it didn't consider temporal structures of event-related potentials which may be very important for single-trial EEG classification. In this paper, we propose a new framework of feature extraction for classification of hand movement imagery EEG. Computer simulations on real experimental data indicate that independent residual analysis (IRA) method can provide efficient temporal features. Combining IRA features with the CSP method, we obtain the optimal spatial and temporal features with which we achieve the best classification rate. The high classification rate indicates that the proposed method is promising for an EEG-based brain-computer interface.

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Automatic channel selection in EEG signals for classification of left or right hand movement in Brain Computer Interfaces using improved binary gravitation search algorithm

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2016.11.018 ◽

2017 ◽

Vol 33 ◽

pp. 109-118 ◽

Cited By ~ 41

Author(s):

Alireza Ghaemi ◽

Esmat Rashedi ◽

Ali Mohammad Pourrahimi ◽

Mehdi Kamandar ◽

Farhad Rahdari

Keyword(s):

Search Algorithm ◽

Hand Movement ◽

Channel Selection ◽

Brain Computer Interfaces ◽

Eeg Signals ◽

Computer Interfaces ◽

Right Hand

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Classification of left and right hand movement from EEG signals by HOS features

2018 9th International Symposium on Signal, Image, Video and Communications (ISIVC) ◽

10.1109/isivc.2018.8709084 ◽

2018 ◽

Author(s):

LAGDALI Salwa ◽

RZIZA mohammed

Keyword(s):

Hand Movement ◽

Eeg Signals ◽

Right Hand ◽

Left And Right

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Development of emotion classifier based on absolute and differential attributes of averaged signals of visually stimulated event related potentials

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331220904889 ◽

2020 ◽

Vol 42 (11) ◽

pp. 2057-2067

Author(s):

Moon Inder Singh ◽

Mandeep Singh

Keyword(s):

Event Related Potentials ◽

Central Line ◽

Support Vector ◽

Eeg Signals ◽

High Arousal ◽

Brain Signals ◽

Polynomial Classifier ◽

Related Potentials ◽

Daunting Challenge

Analysis and study of abstract human relations have always posed a daunting challenge for technocrats engaged in the field of psychometric analysis. The study on emotion recognition is all the more demanding as it involves integration of abstract phenomenon of emotion causation and emotion appraisal through physiological and brain signals. This paper describes the classification of human emotions into four classes, namely: low valence high arousal (LVHA), high valence high arousal (HVHA), high valence low arousal (HVLA) and low valence low arousal (LVLA) using Electroencephalogram (EEG) signals. The EEG signals have been collected on three EEG electrodes along the central line viz: Fz, Cz and Pz. The analysis has been done on average event related potentials (ERPs) and difference of average ERPs using Support Vector Machine (SVM) polynomial classifier. The four-class classification accuracy of 75% using average ERP attributes and an accuracy of 76.8% using difference of ERPs as attributes has been obtained. The accuracy obtained using differential average ERP attributes is better as compared with the already existing studies.

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Extracting Spatio-temporal Feature for Classification of Event-related Potentials*

PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS ◽

10.3724/sp.j.1206.2011.00123 ◽

2011 ◽

Vol 38 (9) ◽

pp. 866-871 ◽

Cited By ~ 1

Author(s):

Zhi-Hua HUANG ◽

Ming-Hong LI ◽

Yuan-Ye MA ◽

Chang-Le ZHOU

Keyword(s):

Event Related Potentials ◽

Related Potentials ◽

Spatio Temporal ◽

Temporal Feature

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ASSESSING THE IMPACT OF CONGRUENT AND INCONGRUENT STIMULUS IN STROOP TASK, USING EVENT-RELATED POTENTIALS (ERP) IN PATIENTS WITH DEPRESSION

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237218500345 ◽

2018 ◽

Vol 30 (05) ◽

pp. 1850034

Author(s):

Yeganeh Shahsavar ◽

Majid Ghoshuni

Keyword(s):

Stroop Task ◽

Time Window ◽

Event Related Potentials ◽

Peak Time ◽

Eeg Signals ◽

Incongruent Stimulus ◽

Related Potentials ◽

Recording Channel ◽

The Mean ◽

The Impact

The main goal of this event-related potentials (ERPs) study was to assess the effects of stimulations in Stroop task in brain activities of patients with different degrees of depression. Eighteen patients (10 males, with the mean age [Formula: see text]) were asked to fill out Beck’s depression questionnaire. Electroencephalographic (EEG) signals of subjects were recorded in three channels (Pz, Cz, and Fz) during Stroop test. This test entailed 360 stimulations, which included 120 congruent, 120 incongruent, and 120 neutral stimulations. To analyze the data, 18 time features in each type of stimulus were extracted from the ERP components and the optimal features were selected. The correlation between the subjects’ scores in Beck’s depression questionnaires and the extracted time features in each recording channel was calculated in order to select the best features. Total area, and peak-to-peak time window in the Cz channel in both the congruent and incongruent stimulus showed significant correlation with Beck scores, with [Formula: see text], [Formula: see text] and [Formula: see text], [Formula: see text], respectively. Consequently, given the correlation between time features and the subjects’ Beck scores with different degrees of depression, it can be interpreted that in case of growth in degrees of depression, stimulations involving congruent images would produce more challenging interferences for the patients compared to incongruent stimulations which can be more effective in diagnosing the level of disorder.

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Design and Verification of a Dry Sensor-Based Multi-Channel Digital Active Circuit for Human Brain Electroencephalography Signal Acquisition Systems

Micromachines ◽

10.3390/mi10110720 ◽

2019 ◽

Vol 10 (11) ◽

pp. 720

Author(s):

Chin-Teng Lin ◽

Chi-Hsien Liu ◽

Po-Sheng Wang ◽

Jung-Tai King ◽

Lun-De Liao

Keyword(s):

Daily Life ◽

Event Related Potentials ◽

Computer Interface ◽

Signal Acquisition ◽

Eeg Signal ◽

Eeg Signals ◽

Preparation Time ◽

Active Circuit ◽

Related Potentials ◽

Common Application

A brain–computer interface (BCI) is a type of interface/communication system that can help users interact with their environments. Electroencephalography (EEG) has become the most common application of BCIs and provides a way for disabled individuals to communicate. While wet sensors are the most commonly used sensors for traditional EEG measurements, they require considerable preparation time, including the time needed to prepare the skin and to use the conductive gel. Additionally, the conductive gel dries over time, leading to degraded performance. Furthermore, requiring patients to wear wet sensors to record EEG signals is considered highly inconvenient. Here, we report a wireless 8-channel digital active-circuit EEG signal acquisition system that uses dry sensors. Active-circuit systems for EEG measurement allow people to engage in daily life while using these systems, and the advantages of these systems can be further improved by utilizing dry sensors. Moreover, the use of dry sensors can help both disabled and healthy people enjoy the convenience of BCIs in daily life. To verify the reliability of the proposed system, we designed three experiments in which we evaluated eye blinking and teeth gritting, measured alpha waves, and recorded event-related potentials (ERPs) to compare our developed system with a standard Neuroscan EEG system.

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Towards an Accessible Use of a Brain-Computer Interfaces-Based Home Care System through a Smartphone

Computational Intelligence and Neuroscience ◽

10.1155/2020/1843269 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17

Author(s):

Koun-Tem Sun ◽

Kai-Lung Hsieh ◽

Syuan-Rong Syu

Keyword(s):

Home Care ◽

Event Related Potentials ◽

End Users ◽

Disabled People ◽

Eeg Signals ◽

Computer Interfaces ◽

Related Potentials ◽

The One ◽

The Brain ◽

Care System

This study proposes a home care system (HCS) based on a brain-computer interface (BCI) with a smartphone. The HCS provides daily help to motor-disabled people when a caregiver is not present. The aim of the study is two-fold: (1) to develop a BCI-based home care system to help end-users control their household appliances, and (2) to assess whether the architecture of the HCS is easy for motor-disabled people to use. A motion-strip is used to evoke event-related potentials (ERPs) in the brain of the user, and the system immediately processes these potentials to decode the user’s intentions. The system, then, translates these intentions into application commands and sends them via Bluetooth to the user’s smartphone to make an emergency call or to execute the corresponding app to emit an infrared (IR) signal to control a household appliance. Fifteen healthy and seven motor-disabled subjects (including the one with ALS) participated in the experiment. The average online accuracy was 81.8% and 78.1%, respectively. Using component N2P3 to discriminate targets from nontargets can increase the efficiency of the system. Results showed that the system allows end-users to use smartphone apps as long as they are using their brain waves. More important, only one electrode O1 is required to measure EEG signals, giving the system good practical usability. The HCS can, thus, improve the autonomy and self-reliance of its end-users.

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