Classification of EEG Mental Patterns by Using Two Scalp Electrodes and Mahalanobis Distance-Based Classifiers

2002 ◽  
Vol 41 (04) ◽  
pp. 337-341 ◽  
Author(s):  
F. Cincotti ◽  
D. Mattia ◽  
C. Babiloni ◽  
F. Carducci ◽  
L. Bianchi ◽  
...  
Keyword(s):  
Mahalanobis Distance ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Correct Classification ◽  
Interface Area ◽  
System A ◽  
The Brain ◽  
Full Covariance Matrix ◽  
Recording Electrodes

Summary Objectives: In this paper, we explored the use of quadratic classifiers based on Mahalanobis distance to detect mental EEG patterns from a reduced set of scalp recording electrodes. Methods: Electrodes are placed in scalp centro-parietal zones (C3, P3, C4 and P4 positions of the international 10-20 system). A Mahalanobis distance classifier based on the use of full covariance matrix was used. Results: The quadratic classifier was able to detect EEG activity related to imagination of movement with an affordable accuracy (97% correct classification, on average) by using only C3 and C4 electrodes. Conclusions: Such a result is interesting for the use of Mahalanobis-based classifiers in the brain computer interface area.

scholarly journals Functional Connectivity and Classification of Actual and Imaginary Motor Movement

2019 ◽  
Vol 9 (2) ◽  
pp. 529-535
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Support Vector ◽  
Motor Movement ◽  
Baseline Activity ◽  
The Individual ◽  
Sensitivity Specificity ◽  
Movement Selection ◽  
The Brain

Imaginary Motor movement is an utmost important for the designing of brain computer interface to assist the individual with physically disability. Brain signals associated with actual motor movement include the signal for muscle activity whereas in case of imaginary motor movement actual muscle movement is not present .Authors have investigated the similarity/dissimilarity between the eeg signals generated in both the cases along with the baseline activity. To instruct the brain computer interface signals generated by electrodes of EEG must resemble with actual motor movement. Selection of electrodes placement plays an important role for this purpose. In this study major four regions of the brain has been covered frontal, temporal, parietal and occipital region of the scalp and features are extracted from the signals are standard deviations, kurtosis, skew and mean. Support Vector Machine is used for the classification between actual and imaginary motor movement along with differentiation between baseline and imaginary motor movement and actual motor movement at 14 different electrodes positions. Statistical performances of the classifier have been evaluated by computing sensitivity, specificity and accuracy. The location involved to achieve maximum accuracy for the classification of motor movements (actual and imaginary) and no motor movement is at frontal, temporal and parietal region

scholarly journals A Deep Classifier for Upper-Limbs Motor Anticipation Tasks in an Online BCI Setting

2021 ◽  
Vol 8 (2) ◽  
pp. 21
Author(s):  
Andrea Valenti ◽  
Michele Barsotti ◽  
Davide Bacciu ◽  
Luca Ascari
Keyword(s):  
Real Time ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Non Invasive ◽  
The Brain ◽  
Movement Intention

Decoding motor intentions from non-invasive brain activity monitoring is one of the most challenging aspects in the Brain Computer Interface (BCI) field. This is especially true in online settings, where classification must be performed in real-time, contextually with the user’s movements. In this work, we use a topology-preserving input representation, which is fed to a novel combination of 3D-convolutional and recurrent deep neural networks, capable of performing multi-class continual classification of subjects’ movement intentions. Our model is able to achieve a higher accuracy than a related state-of-the-art model from literature, despite being trained in a much more restrictive setting and using only a simple form of input signal preprocessing. The results suggest that deep learning models are well suited for deployment in challenging real-time BCI applications such as movement intention recognition.

Simultaneous Classification of Multiple Motor Imagery and P300 for Increase in Output Information of Brain-computer Interface

2013 ◽  
Vol 133 (3) ◽  
pp. 635-641
Author(s):  
Genzo Naito ◽  
Lui Yoshida ◽  
Takashi Numata ◽  
Yutaro Ogawa ◽  
Kiyoshi Kotani ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Output Information

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

Research of “Yes” and “No” Responses by Auditory Stimuli in Human EEG

2013 ◽  
Vol 310 ◽  
pp. 660-664 ◽  
Author(s):  
Zi Guang Li ◽  
Guo Zhong Liu
Keyword(s):  
Communication Channel ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Auditory Stimuli ◽  
Support Vector ◽  
Quality Life ◽  
Asking Questions ◽  
Command Signals ◽  
Different Characteristics ◽  
The Brain

As an emerging technology, brain-computer interface (BCI) bring us a novel communication channel which translate brain activities into command signals for devices like computer, prosthesis, robots, and so forth. The aim of the brain-computer interface research is to improve the quality life of patients who are suffering from server neuromuscular disease. This paper focus on analyzing the different characteristics of the brainwaves when a subject responses “yes” or “no” to auditory stimulation questions. The experiment using auditory stimuli of form of asking questions is adopted. The extraction of the feature adopted the method of common spatial patterns(CSP) and the classification used support vector machine (SVM) . The classification accuracy of "yes" and "no" answers achieves 80.2%. The experiment result shows the feasibility and effectiveness of this solution and provides a basis for advanced research .

scholarly journals A comparative study: use of a Brain-computer Interface (BCI) device by people with cerebral palsy in interaction with computers

2015 ◽  
Vol 87 (4) ◽  
pp. 1929-1937 ◽  
Author(s):  
Regina O. Heidrich ◽  
Emely Jensen ◽  
Francisco Rebelo ◽  
Tiago Oliveira
Keyword(s):  
Cerebral Palsy ◽  
Comparative Study ◽  
Case Studies ◽  
Brain Computer Interface ◽  
Product Evaluation ◽  
Computer Interface ◽  
Healthy Controls ◽  
The Brain

ABSTRACT This article presents a comparative study among people with cerebral palsy and healthy controls, of various ages, using a Brain-computer Interface (BCI) device. The research is qualitative in its approach. Researchers worked with Observational Case Studies. People with cerebral palsy and healthy controls were evaluated in Portugal and in Brazil. The study aimed to develop a study for product evaluation in order to perceive whether people with cerebral palsy could interact with the computer and compare whether their performance is similar to that of healthy controls when using the Brain-computer Interface. Ultimately, it was found that there are no significant differences between people with cerebral palsy in the two countries, as well as between populations without cerebral palsy (healthy controls).

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