Local Discriminant Wavelet Packet Basis for Signal Classification in Brain Computer Interface

2015 ◽  
pp. 584-587 ◽  
Author(s):  
Victoria Peterson ◽  
Rubén Acevedo ◽  
Hugo Leonardo Rufiner ◽  
Rubén Spies
Keyword(s):  
Brain Computer Interface ◽  
Wavelet Packet ◽  
Signal Classification ◽  
Computer Interface

An Efficient Deep Learning Paradigm for Deceit Identification Test on EEG Signals

2021 ◽  
Vol 12 (3) ◽  
pp. 1-20
Author(s):  
Damodar Reddy Edla ◽  
Shubham Dodia ◽  
Annushree Bablani ◽  
Venkatanareshbabu Kuppili
Keyword(s):  
Brain Computer Interface ◽  
Wavelet Packet ◽  
Stimulus Onset ◽  
Computer Interface ◽  
Band Pass Filter ◽  
Eeg Signals ◽  
Pass Filter ◽  
Eeg Data ◽  
Identification Test ◽  
Band Pass

Brain-Computer Interface is the collaboration of the human brain and a device that controls the actions of a human using brain signals. Applications of brain-computer interface vary from the field of entertainment to medical. In this article, a novel Deceit Identification Test is proposed based on the Electroencephalogram signals to identify and analyze the human behavior. Deceit identification test is based on P300 signals, which have a positive peak from 300 ms to 1,000 ms of the stimulus onset. The aim of the experiment is to identify and classify P300 signals with good classification accuracy. For preprocessing, a band-pass filter is used to eliminate the artifacts. The feature extraction is carried out using “symlet” Wavelet Packet Transform (WPT). Deep Neural Network (DNN) with two autoencoders having 10 hidden layers each is applied as the classifier. A novel experiment is conducted for the collection of EEG data from the subjects. EEG signals of 30 subjects (15 guilty and 15 innocent) are recorded and analyzed during the experiment. BrainVision recorder and analyzer are used for recording and analyzing EEG signals. The model is trained for 90% of the dataset and tested for 10% of the dataset and accuracy of 95% is obtained.

scholarly journals Investigation of EEG Signal Classification Techniques for Brain Computer Interface

2020 ◽  
Vol 9 (3) ◽  
pp. 3266-3274
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Brain Computer Interface ◽  
Signal Classification ◽  
Computer Interface ◽  
Support Vector ◽  
Eeg Signals ◽  
Classification Techniques ◽  
Feature Extraction And Selection ◽  
Bci System

Brain-computer interface (BCI) has emerged as a popular research domain in recent years. The use of electroencephalography (EEG) signals for motor imagery (MI) based BCI has gained widespread attention. The first step in its implementation is to fetch EEG signals from scalp of human subject. The preprocessing of EEG signals is done before applying feature extraction, selection and classification techniques as main steps of signal processing. In preprocessing stage, artifacts are removed from raw brain signals before these are input to next stage of feature extraction. Subsequently classifier algorithms are used to classify selected features into intended MI tasks. The major challenge in a BCI systems is to improve classification accuracy of a BCI system. In this paper, an approach based on Support Vector Machine (SVM), is proposed for signal classification to improve accuracy of the BCI system. The parameters of kernel are varied to attain improvement in classification accuracy. Independent component analysis (ICA) technique is used for preprocessing and filter bank common spatial pattern (FBCSP) for feature extraction and selection. The proposed approach is evaluated on data set 2a of BCI Competition IV by using 5-fold crossvalidation procedure. Results show that it performs better in terms of classification accuracy, as compared to other methods reported in literature.

Sign in / Sign up

Export Citation Format

Share Document