Adaptive classification to reduce non-stationarity in visual evoked potential brain-computer interfaces

2019 ◽  
Vol 15 (2) ◽  
Author(s):  
Deepak Kapgate ◽  
Dhananjay Kalbande ◽  
Urmila Shrawankar
Keyword(s):  
Data Sets ◽  
Mean Values ◽  
Linear Discriminant ◽  
Variance Matrix ◽  
Adaptive Classification ◽  
Computer Interfaces ◽  
Classifier Performance ◽  
Electroencephalogram Eeg ◽  
Class Pair ◽  
Visual Evoked

Abstract Non-stationarity of electroencephalogram (EEG) signals greatly affect classifier performance in brain-computer interface (BCI). To overcome this problem we propose an adaptive classifier model known as extended multi-class pooled mean linear discriminant analysis (EMPMLDA). Here, we update the average class pair co-variance matrix along with pooled mean values. Evaluation of classifiers are done on visual evoked cortical potential data-sets. We demonstrate that EMPMLDA can significantly outperform other static classifiers such as MLDA and adaptive classifiers (MPMLDA). Furthermore an optimal update coefficient can be achieved using different datasets.

scholarly journals Adaptive Multiclass Classification for Brain Computer Interfaces

2014 ◽  
Vol 26 (6) ◽  
pp. 1108-1127 ◽  
Author(s):  
A. Llera ◽  
V. Gómez ◽  
H. J. Kappen
Keyword(s):  
Adaptive Methods ◽  
Data Sets ◽  
Brain Computer Interfaces ◽  
Linear Discriminant ◽  
Adaptive Classification ◽  
Computer Interfaces ◽  
Learning Rates ◽  
Eeg Data ◽  
Using Data ◽  
Efficient Learning

We consider the problem of multiclass adaptive classification for brain-computer interfaces and propose the use of multiclass pooled mean linear discriminant analysis (MPMLDA), a multiclass generalization of the adaptation rule introduced by Vidaurre, Kawanabe, von Bünau, Blankertz, and Müller ( 2010 ) for the binary class setting. Using publicly available EEG data sets and tangent space mapping (Barachant, Bonnet, Congedo, & Jutten, 2012 ) as a feature extractor, we demonstrate that MPMLDA can significantly outperform state-of-the-art multiclass static and adaptive methods. Furthermore, efficient learning rates can be achieved using data from different subjects.

scholarly journals Robot Motion Control Using the Emotiv EPOC EEG System

2018 ◽  
Vol 7 (2) ◽  
pp. 279-285
Author(s):  
Sandy Akbar Dewangga ◽  
Handayani Tjandrasa ◽  
Darlis Herumurti
Keyword(s):  
Robot Control ◽  
Computer Interface ◽  
Support Vector ◽  
Robot Motion ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
Computer Interfaces ◽  
Average Accuracy ◽  
The Subject ◽  
Electroencephalogram Eeg

Brain-computer interfaces have been explored for years with the intent of using human thoughts to control mechanical system. By capturing the transmission of signals directly from the human brain or electroencephalogram (EEG), human thoughts can be made as motion commands to the robot. This paper presents a prototype for an electroencephalogram (EEG) based brain-actuated robot control system using mental commands. In this study, Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) method were combined to establish the best model. Dataset containing features of EEG signals were obtained from the subject non-invasively using Emotiv EPOC headset. The best model was then used by Brain-Computer Interface (BCI) to classify the EEG signals into robot motion commands to control the robot directly. The result of the classification gave the average accuracy of 69.06%.

scholarly journals Excessive Crossed Disparity Detection by Visual Evoked Potentials to Reduce Visual Discomfort in 3D Viewing

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Xiao Wang ◽  
Liuye Yao ◽  
Zhiyu Qian ◽  
Lidong Xing ◽  
Weitao Li ◽  
...  
Keyword(s):  
Evoked Potentials ◽  
Visual Evoked Potentials ◽  
Experimental System ◽  
Occipital Lobe ◽  
Cost Effective ◽  
Subjective Assessment ◽  
Classification Model ◽  
Visual Discomfort ◽  
Linear Discriminant ◽  
Visual Evoked

As excessive crossed disparity is known to cause visual discomfort, this study aims to establish a classification model to discriminate excessive crossed disparity in stereoscopic viewing in combination with subjective assessment of visual discomfort. A stereo-visual evoked potentials (VEPs) experimental system was built up to obtain the VEPs evoked by stereoscopic stimulus with different disparities. Ten volunteers participated in this experiment, and forty VEP datasets in total were extracted when the viewers were under comfortable viewing conditions. Six features of VEPs from three electrodes at the occipital lobe were chosen, and the classification was established using the Fisher’s linear discriminant (FLD). Based on FLD results, the correct rate for determining the excessive crossed disparity was 70%, and it reached 80% for other stimuli. The study demonstrated cost-effective discriminant classification modelling to distinguish the stimulus with excessive crossed disparity which inclines to cause visual discomfort.

Using the Kullback-Leibler Divergence and Kolmogorov-Smirnov Test to Select Input Sizes to the Fault Diagnosis Problem Based on a CNN Model

2021 ◽  
Vol 18 (2) ◽  
pp. 16-26
Author(s):  
Rodrigo Paula Monteiro ◽  
◽  
Carmelo Jose Albanez Bastos-Filho ◽  
Mariela Cerrada ◽  
Diego Cabrera ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Classification Model ◽  
Data Sets ◽  
Frequency Spectra ◽  
Signal Representations ◽  
Input Size ◽  
Leibler Divergence ◽  
Classifier Performance ◽  
Kolmogorov Smirnov ◽  
Smirnov Test

Choosing a suitable size for signal representations, e.g., frequency spectra, in a given machine learning problem is not a trivial task. It may strongly affect the performance of the trained models. Many solutions have been proposed to solve this problem. Most of them rely on designing an optimized input or selecting the most suitable input according to an exhaustive search. In this work, we used the Kullback-Leibler Divergence and the Kolmogorov-Smirnov Test to measure the dissimilarity among signal representations belonging to equal and different classes, i.e., we measured the intraclass and interclass dissimilarities. Moreover, we analyzed how this information relates to the classifier performance. The results suggested that both the interclass and intraclass dissimilarities were related to the model accuracy since they indicate how easy a model can learn discriminative information from the input data. The highest ratios between the average interclass and intraclass dissimilarities were related to the most accurate classifiers. We can use this information to select a suitable input size to train the classification model. The approach was tested on two data sets related to the fault diagnosis of reciprocating compressors.

A Review of Current Approaches of Brain Computer Interfaces

2013 ◽  
pp. 1516-1534
Author(s):  
Lochi Yu ◽  
Cristian Ureña
Keyword(s):  
Brain Computer Interface ◽  
Severe Disabilities ◽  
Extraction Methods ◽  
Support Vector ◽  
Brain Electrical Activity ◽  
Linear Discriminant ◽  
Computer Interfaces ◽  
Vector Machines ◽  
Computational Resources ◽  
The Brain

Since the first recordings of brain electrical activity more than 100 years ago remarkable contributions have been done to understand the brain functionality and its interaction with environment. Regardless of the nature of the brain-computer interface BCI, a world of opportunities and possibilities has been opened not only for people with severe disabilities but also for those who are pursuing innovative human interfaces. Deeper understanding of the EEG signals along with refined technologies for its recording is helping to improve the performance of EEG based BCIs. Better processing and features extraction methods, like Independent Component Analysis (ICA) and Wavelet Transform (WT) respectively, are giving promising results that need to be explored. Different types of classifiers and combination of them have been used on EEG BCIs. Linear, neural and nonlinear Bayesian have been the most used classifiers providing accuracies ranges between 60% and 90%. Some demand more computational resources like Support Vector Machines (SVM) classifiers but give good generality. Linear Discriminant Analysis (LDA) classifiers provide poor generality but low computational resources, making them optimal for some real time BCIs. Better classifiers must be developed to tackle the large patterns variability across different subjects by using every available resource, method or technology.

Semisupervised Deep Stacking Network with Adaptive Learning Rate Strategy for Motor Imagery EEG Recognition

2019 ◽  
Vol 31 (5) ◽  
pp. 919-942 ◽  
Author(s):  
Xian-Lun Tang ◽  
Wei-Chang Ma ◽  
De-Song Kong ◽  
Wei Li
Keyword(s):  
Supervised Learning ◽  
Motor Imagery ◽  
Adaptive Learning ◽  
Recognition Accuracy ◽  
Learning Rate ◽  
Data Sets ◽  
Adaptive Learning Rate ◽  
Eeg Data ◽  
Contrastive Divergence ◽  
Electroencephalogram Eeg

Practical motor imagery electroencephalogram (EEG) data-based applications are limited by the waste of unlabeled samples in supervised learning and excessive time consumption in the pretraining period. A semisupervised deep stacking network with an adaptive learning rate strategy (SADSN) is proposed to solve the sample loss caused by supervised learning of EEG data and the extraction of manual features. The SADSN adopts the idea of an adaptive learning rate into a contrastive divergence (CD) algorithm to accelerate its convergence. Prior knowledge is introduced into the intermediary layer of the deep stacking network, and a restricted Boltzmann machine is trained by a semisupervised method in which the adjusting scope of the coefficient in learning rate is determined by performance analysis. Several EEG data sets are carried out to evaluate the performance of the proposed method. The results show that the recognition accuracy of SADSN is advanced with a more significant convergence rate and successfully classifies motor imagery.

scholarly journals Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4629 ◽  
Author(s):  
Ciaran Cooney ◽  
Attila Korik ◽  
Raffaella Folli ◽  
Damien Coyle
Keyword(s):  
Deep Learning ◽  
Support Vector ◽  
Great Success ◽  
Hyperparameter Optimization ◽  
Linear Discriminant ◽  
Classifier Performance ◽  
Open Question ◽  
The Impact ◽  
Regularized Linear Discriminant Analysis

Classification of electroencephalography (EEG) signals corresponding to imagined speech production is important for the development of a direct-speech brain–computer interface (DS-BCI). Deep learning (DL) has been utilized with great success across several domains. However, it remains an open question whether DL methods provide significant advances over traditional machine learning (ML) approaches for classification of imagined speech. Furthermore, hyperparameter (HP) optimization has been neglected in DL-EEG studies, resulting in the significance of its effects remaining uncertain. In this study, we aim to improve classification of imagined speech EEG by employing DL methods while also statistically evaluating the impact of HP optimization on classifier performance. We trained three distinct convolutional neural networks (CNN) on imagined speech EEG using a nested cross-validation approach to HP optimization. Each of the CNNs evaluated was designed specifically for EEG decoding. An imagined speech EEG dataset consisting of both words and vowels facilitated training on both sets independently. CNN results were compared with three benchmark ML methods: Support Vector Machine, Random Forest and regularized Linear Discriminant Analysis. Intra- and inter-subject methods of HP optimization were tested and the effects of HPs statistically analyzed. Accuracies obtained by the CNNs were significantly greater than the benchmark methods when trained on both datasets (words: 24.97%, p < 1 × 10–7, chance: 16.67%; vowels: 30.00%, p < 1 × 10–7, chance: 20%). The effects of varying HP values, and interactions between HPs and the CNNs were both statistically significant. The results of HP optimization demonstrate how critical it is for training CNNs to decode imagined speech.

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