scholarly journals Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller

2021 ◽  
Vol 11 (23) ◽  
pp. 11252
Author(s):  
Ayana Mussabayeva ◽  
Prashant Kumar Jamwal ◽  
Muhammad Tahir Akhtar
Keyword(s):  
Healthy Subjects ◽  
Single Channel ◽  
Event Related Potentials ◽  
Support Vector ◽  
Ensemble Classifiers ◽  
K Nearest Neighbors ◽  
Linear Discriminant ◽  
P300 Speller ◽  
Related Potentials ◽  
Als Patients

Classification of brain signal features is a crucial process for any brain–computer interface (BCI) device, including speller systems. The positive P300 component of visual event-related potentials (ERPs) used in BCI spellers has individual variations of amplitude and latency that further changse with brain abnormalities such as amyotrophic lateral sclerosis (ALS). This leads to the necessity for the users to train the speller themselves, which is a very time-consuming procedure. To achieve subject-independence in a P300 speller, ensemble classifiers are proposed based on classical machine learning models, such as the support vector machine (SVM), linear discriminant analysis (LDA), k-nearest neighbors (kNN), and the convolutional neural network (CNN). The proposed voters were trained on healthy subjects’ data using a generic training approach. Different combinations of electroencephalography (EEG) channels were used for the experiments presented, resulting in single-channel, four-channel, and eight-channel classification. ALS patients’ data represented robust results, achieving more than 90% accuracy when using an ensemble of LDA, kNN, and SVM on four active EEG channels data in the occipital area of the brain. The results provided by the proposed ensemble voting models were on average about 5% more accurate than the results provided by the standalone classifiers. The proposed ensemble models could also outperform boosting algorithms in terms of computational complexity or accuracy. The proposed methodology shows the ability to be subject-independent, which means that the system trained on healthy subjects can be efficiently used for ALS patients. Applying this methodology for online speller systems removes the necessity to retrain the P300 speller.

scholarly journals Multiclass classification of motor imagery EEG signals using ensemble classifiers & cross-correlation

2018 ◽  
Vol 7 (2.6) ◽  
pp. 163
Author(s):  
D Hari Krishna ◽  
I A.Pasha ◽  
T Satya Savithri
Keyword(s):  
Cross Correlation ◽  
Signal To Noise Ratio ◽  
Majority Voting ◽  
Support Vector ◽  
Band Pass Filter ◽  
Eeg Signal ◽  
Ensemble Classifiers ◽  
K Nearest Neighbors ◽  
Pass Filter ◽  
Linear Discriminant

To communicate without any muscle movement and purely based on brain signal has been the goal of Brain computer interfacing (BCI). Recent BCI based studies reported more and more accurate detection of brain states. This paper proposes a study that detects EEG signal belonging todifferent imaginary motor activities (Right leg, right hand, left leg and left hand). The Electroencephalogram (EEG) signal has been conditioned by band pass filter (BPF) to improve signal to noise ratio (SNR). The proposed method is based on similarity between signals to extract features. For measuring the similarity between signals, Cross correlation (CC) is used. An ensemble set of five classifiers (Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naïve Bayes (NB) and Binary Decision Tree) was used collectively.  As the similarity measurement was binary in nature, one versus rest (OVR) approach was used for multi class classification. Random subset of features was used to train the ensemble of classifiers. The classification label was obtained by using majority voting. An average accuracy of 89.57% was observed among all 10 subjects.

scholarly journals A new approach based on principal ERPs and LDA to improve P300 mind spellers

2021 ◽  
Author(s):  
Ali Mobaien ◽  
Negar Kheirandish ◽  
Reza Boostani
Keyword(s):  
Event Related Potentials ◽  
Training Data ◽  
Support Vector ◽  
New Approach ◽  
Linear Discriminant ◽  
Brain Signals ◽  
Vector Machines ◽  
Related Potentials ◽  
The Brain ◽  
Visual P300

<div>Abstract—Visual P300 mind speller is a brain-computer interface that allows an individual to type through his mind. For this goal, the subject sits in front of a screen full of characters, and when his desired one is highlighted, there will be a P300 response (a positive deflection nearly 300ms after stimulus) in his brain signals. Due to the very low signal-to noise (SNR) of the P300 in the background activities of the brain, detection of this component is challenging. Principal ERP reduction (pERP-RED) is a newly developed method that can effectively extract the underlying templates of event-related potentials (ERPs), by employing a three-step spatial filtering procedure. In this research, we investigate the performance of pERP-RED in conjunction with linear discriminant analysis (LDA) to classify P300 data. The proposed method is examined on a real P300 dataset and compared to the state-of-the-art LDA and support vector machines. The results demonstrate that the proposed method achieves higher classification accuracy in low SNRs and low numbers of training data.</div>

scholarly journals A new approach based on principal ERPs and LDA to improve P300 mind spellers

2021 ◽  
Author(s):  
Ali Mobaien ◽  
Negar Kheirandish ◽  
Reza Boostani
Keyword(s):  
Event Related Potentials ◽  
Training Data ◽  
Support Vector ◽  
New Approach ◽  
Linear Discriminant ◽  
Brain Signals ◽  
Vector Machines ◽  
Related Potentials ◽  
The Brain ◽  
Visual P300

<div>Abstract—Visual P300 mind speller is a brain-computer interface that allows an individual to type through his mind. For this goal, the subject sits in front of a screen full of characters, and when his desired one is highlighted, there will be a P300 response (a positive deflection nearly 300ms after stimulus) in his brain signals. Due to the very low signal-to noise (SNR) of the P300 in the background activities of the brain, detection of this component is challenging. Principal ERP reduction (pERP-RED) is a newly developed method that can effectively extract the underlying templates of event-related potentials (ERPs), by employing a three-step spatial filtering procedure. In this research, we investigate the performance of pERP-RED in conjunction with linear discriminant analysis (LDA) to classify P300 data. The proposed method is examined on a real P300 dataset and compared to the state-of-the-art LDA and support vector machines. The results demonstrate that the proposed method achieves higher classification accuracy in low SNRs and low numbers of training data.</div>

scholarly journals Improving P300 Speller performance by means of optimization and machine learning

2021 ◽  
Author(s):  
Luigi Bianchi ◽  
Chiara Liti ◽  
Giampaolo Liuzzi ◽  
Veronica Piccialli ◽  
Cecilia Salvatore
Keyword(s):  
Machine Learning ◽  
Signal To Noise Ratio ◽  
Event Related Potentials ◽  
Support Vector ◽  
Early Stopping ◽  
Signal To Noise ◽  
Linear Discriminant ◽  
Related Potentials ◽  
Noise Ratio ◽  
Number Of Iterations

AbstractBrain-Computer Interfaces (BCIs) are systems allowing people to interact with the environment bypassing the natural neuromuscular and hormonal outputs of the peripheral nervous system (PNS). These interfaces record a user’s brain activity and translate it into control commands for external devices, thus providing the PNS with additional artificial outputs. In this framework, the BCIs based on the P300 Event-Related Potentials (ERP), which represent the electrical responses recorded from the brain after specific events or stimuli, have proven to be particularly successful and robust. The presence or the absence of a P300 evoked potential within the EEG features is determined through a classification algorithm. Linear classifiers such as stepwise linear discriminant analysis and support vector machine (SVM) are the most used discriminant algorithms for ERPs’ classification. Due to the low signal-to-noise ratio of the EEG signals, multiple stimulation sequences (a.k.a. iterations) are carried out and then averaged before the signals being classified. However, while augmenting the number of iterations improves the Signal-to-Noise Ratio, it also slows down the process. In the early studies, the number of iterations was fixed (no stopping environment), but recently several early stopping strategies have been proposed in the literature to dynamically interrupt the stimulation sequence when a certain criterion is met in order to enhance the communication rate. In this work, we explore how to improve the classification performances in P300 based BCIs by combining optimization and machine learning. First, we propose a new decision function that aims at improving classification performances in terms of accuracy and Information Transfer Rate both in a no stopping and early stopping environment. Then, we propose a new SVM training problem that aims to facilitate the target-detection process. Our approach proves to be effective on several publicly available datasets.

Branched chain amino acids decrease top-down event-related potentials in healthy subjects

2007 ◽  
Vol 40 (05) ◽  
Author(s):  
AH Neuhaus ◽  
TE Goldberg ◽  
Y Hassoun ◽  
JA Bates ◽  
KW Nassauer ◽  
...  
Keyword(s):  
Amino Acids ◽  
Healthy Subjects ◽  
Event Related Potentials ◽  
Branched Chain Amino Acids ◽  
Top Down ◽  
Related Potentials ◽  
Branched Chain

scholarly journals Effects of handedness on olfactory event-related potentials in a simple olfactory task

2015 ◽  
Vol 53 (2) ◽  
pp. 149-153
Author(s):  
Marie Gottschlich ◽  
Thomas Hummel
Keyword(s):  
Hydrogen Sulfide ◽  
Ethyl Alcohol ◽  
Healthy Subjects ◽  
Event Related Potentials ◽  
Major Influence ◽  
Chemical Senses ◽  
Phenyl Ethyl Alcohol ◽  
Related Potentials ◽  
The Rose

The purpose of the present study was to re-investigate the influence of handedness on simple olfactory tasks to further clarify the role of handedness in chemical senses. Similar to language and other sensory systems, effects of handedness should be expected. Young, healthy subjects participated in this study, including 24 left-handers and 24 right-handers, with no indication of any major nasal or health problems. The two groups did not differ in terms of sex and age (14 women and 10 men in each group). They had a mean age of 24.0 years. Olfactory event-related potentials were recorded after left or right olfactory stimulation with the rose-like odor phenyl ethyl alcohol (PEA) or the smell of rotten eggs (hydrogen sulfide, H2S). Results suggested that handedness has no major influence on amplitude or latency of olfactory event-related potentials when it comes to simple olfactory tasks.

scholarly journals Detection of 2D and 3D Video Transitions Based on EEG Power

2020 ◽  
Author(s):  
Negin Manshouri ◽  
Mesut Melek ◽  
Temel Kayikcioglu
Keyword(s):  
Steady State ◽  
Support Vector ◽  
Success Rates ◽  
K Nearest Neighbors ◽  
3D Vision ◽  
Linear Discriminant ◽  
Parietal Lobes ◽  
Brain Signals ◽  
Sudden Transition ◽  
2D And 3D

Despite the long and extensive history of 3D technology, it has recently attracted the attention of researchers. This technology has become the center of interest of young people because of the real feelings and sensations it creates. People see their environment as 3D because of their eye structure. In this study, it is hypothesized that people lose their perception of depth during sleepy moments and that there is a sudden transition from 3D vision to 2D vision. Regarding these transitions, the EEG signal analysis method was used for deep and comprehensive analysis of 2D and 3D brain signals. In this study, a single-stream anaglyph video of random 2D and 3D segments was prepared. After watching this single video, the obtained EEG recordings were considered for two different analyses: the part involving the critical transition (transition-state) and the state analysis of only the 2D versus 3D or 3D versus 2D parts (steady-state). The main objective of this study is to see the behavioral changes of brain signals in 2D and 3D transitions. To clarify the impacts of the human brain&rsquo;s power spectral density (PSD) in 2D-to-3D (2D_3D) and 3D-to-2D (3D_2D) transitions of anaglyph video, 9 visual healthy individuals were prepared for testing in this pioneering study. Spectrogram graphs based on Short Time Fourier transform (STFT) were considered to evaluate the power spectrum analysis in each EEG channel of transition or steady-state. Thus, in 2D and 3D transition scenarios, important channels representing EEG frequency bands and brain lobes will be identified. To classify the 2D and 3D transitions, the dominant bands and time intervals representing the maximum difference of PSD were selected. Afterward, effective features were selected by applying statistical methods such as standard deviation (SD), maximum (max), and Hjorth parameters to epochs indicating transition intervals. Ultimately, k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) algorithms were applied to classify 2D_3D and 3D_2D transitions. The frontal, temporal, and partially parietal lobes show 2D_3D and 3D_2D transitions with a good classification success rate. Overall, it was found that Hjorth parameters and LDA algorithms have 71.11% and 77.78% classification success rates for transition and steady-state, respectively.

scholarly journals Identification of optimum segment in single channel EEG biometric system

2021 ◽  
Vol 23 (3) ◽  
pp. 1847
Author(s):  
Muhammad Afif Hendrawan ◽  
Pramana Yoga Saputra ◽  
Cahya Rahmad
Keyword(s):  
Single Channel ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Alpha Band ◽  
Biometric System ◽  
Linear Discriminant ◽  
Power Spectral ◽  
Eyes Open ◽  
High Level

Nowadays, biometric modalities have gained popularity in security systems. Nevertheless, the conventional commercial-grade biometric system addresses some issues. The biggest problem is that they can be imposed by artificial biometrics. The electroencephalogram (EEG) is a possible solution. It is nearly impossible to replicate because it is dependent on human mental activity. Several studies have already demonstrated a high level of accuracy. However, it requires a large number of sensors and time to collect the signal. This study proposed a biometric system using single-channel EEG recorded during resting eyes open (EO) conditions. A total of 45 EEG signals from 9 subjects were collected. The EEG signal was segmented into 5 second lengths. The alpha band was used in this study. Discrete wavelet transform (DWT) with Daubechies type 4 (db4) was employed to extract the alpha band. Power spectral density (PSD) was extracted from each segment as the main feature. Linear discriminant analysis (LDA) and support vector machine (SVM) were used to classify the EEG signal. The proposed method achieved 86% accuracy using LDA only from the third segment. Therefore, this study showed that it is possible to utilize single-channel EEG during a resting EO state in a biometric system.

Sign in / Sign up

Export Citation Format

Share Document