Brain Computer Interface applications and classification techniques

Shashibala, T.; Gawali, Bharti W.

doi:10.18535/ijecs/v5i7.14

Cited by 3 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LDA is a dimensionality reduction model that works on the concept of minimizing the ratio of within-class scatter to between-class scatter while keeping the intrinsic details of the data intact (Shashibala & Gawali, 2016). Hence, LDA creates a hyperplane in the feature space based on evaluation of the training data to maximize the distance between the two classes and minimize the variance of the same class (Aydemir & Kayikcioglu, 2013; Hasan et al, 2015).…”

Section: Methodsmentioning

confidence: 99%

“…The output of the CSP filter is then used as an input for a ML algorithm, such as linear discriminant analysis (LDA), support vector machine (SVM), or logistic regression (LR) to distinguish EEG patterns associated with motor imageries (Miao et al, 2020). LDA is a very popular model for binary classification of the MI task (Yuksel & Olmez, 2015); it works on the concept of minimizing the ratio of within-class scatter to between-class scatter while keeping the intrinsic details of the data intact (Shashibala & Gawali, 2016). Hence, LDA creates a hyperplane in the feature space based on evaluation of the training data to maximize the distance between the two classes and minimize the variance of the same class (Aydemir & Kayikcioglu, 2013; Hasan et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Promise of Deep Learning for BCIs: Classification of Motor Imagery EEG using Convolutional Neural Network

Leeuwis

Alimardani

2021

Preprint

View full text Add to dashboard Cite

Motor Imagery (MI) is a mental process by which an individual rehearses body movements without actually performing physical actions. Motor Imagery Brain-Computer Interfaces (MI-BCIs) are AI-driven systems that capture brain activity patterns associated with this mental process and convert them into commands for external devices. Traditionally, MI-BCIs operate on Machine Learning (ML) algorithms, which require extensive signal processing and feature engineering to extract changes in sensorimotor rhythms (SMR). However, in recent years, Deep Learning (DL) models have gained popularity for EEG classification as they provide a solution for automatic extraction of spatio-temporal features in the signals. In this study, EEG signals from 54 subjects who performed a MI task of left- or right-hand grasp was employed to compare the performance of two MI-BCI classifiers; a ML approach vs. a DL approach. In the ML approach, Common Spatial Patterns (CSP) was used for feature extraction and then Linear Discriminant Analysis (LDA) model was employed for binary classification of the MI task. In the DL approach, a Convolutional Neural Network (CNN) model was constructed on the raw EEG signals. The mean classification accuracies achieved by the CNN and CSP+LDA models were 69.42% and 52.56%, respectively. Further analysis showed that the DL approach improved the classification accuracy for all subjects within the range of 2.37 to 28.28% and that the improvement was significantly stronger for low performers. Our findings show promise for employment of DL models in future MI-BCI systems, particularly for BCI inefficient users who are unable to produce desired sensorimotor patterns for conventional ML approaches.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Promise of Deep Learning for BCIs: Classification of Motor Imagery EEG using Convolutional Neural Network

Leeuwis

Alimardani

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…LDA is a dimensionality reduction model that works on the concept of minimizing the ratio of within-class scatter to between-class scatter while keeping the intrinsic details of the data intact [46]. Hence, LDA creates a hyperplane in the feature space based on evaluation of the training data to maximize the distance between the two classes and minimize the variance of the same class [47,48].…”

Section: Machine Learning Modelmentioning

confidence: 99%

Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users

Leeuwis¹,

Alimardani²

2022

PLoS ONE

View full text Add to dashboard Cite

Motor Imagery Brain-Computer Interfaces (MI-BCIs) are AI-driven systems that capture brain activity patterns associated with mental imagination of movement and convert them into commands for external devices. Traditionally, MI-BCIs operate on Machine Learning (ML) algorithms, which require extensive signal processing and feature engineering to extract changes in sensorimotor rhythms (SMR). In recent years, Deep Learning (DL) models have gained popularity for EEG classification as they provide a solution for automatic extraction of spatio-temporal features in the signals. However, past BCI studies that employed DL models, only attempted them with a small group of participants, without investigating the effectiveness of this approach for different user groups such as inefficient users. BCI inefficiency is a known and unsolved problem within BCI literature, generally defined as the inability of the user to produce the desired SMR patterns for the BCI classifier. In this study, we evaluated the effectiveness of DL models in capturing MI features particularly in the inefficient users. EEG signals from 54 subjects who performed a MI task of left- or right-hand grasp were recorded to compare the performance of two classification approaches; a ML approach vs. a DL approach. In the ML approach, Common Spatial Patterns (CSP) was used for feature extraction and then Linear Discriminant Analysis (LDA) model was employed for binary classification of the MI task. In the DL approach, a Convolutional Neural Network (CNN) model was constructed on the raw EEG signals. Additionally, subjects were divided into high vs. low performers based on their online BCI accuracy and the difference between the two classifiers’ performance was compared between groups. Our results showed that the CNN model improved the classification accuracy for all subjects within the range of 2.37 to 28.28%, but more importantly, this improvement was significantly larger for low performers. Our findings show promise for employment of DL models on raw EEG signals in future MI-BCI systems, particularly for BCI inefficient users who are unable to produce desired sensorimotor patterns for conventional ML approaches.

show abstract

“…They are placed externally on the scalp using pre-dampened felt pads to conduct the signal through the skin. Due to this, the EPOC is considered a non-invasive type of BCI [1].…”

Section: Experimental Setup and Stimulimentioning

confidence: 99%

“…Electroencephalography (EEG) is a technology that uses electrodes placed on (non-invasive) or in/under (invasive) the skin, skull, or even the brain to record the brain's electrical activity [1]. While traditional EEG setups are most commonly used and associated with the medical and neuroscience research community, Brain-Computer Interfaces (BCIs) have been used in a more general and non-medical manner to record these EEG signals without having to resort to a medical procedure or lab setup.…”

Section: Introductionmentioning

confidence: 99%

Excuse Me, Do I Know You From Somewhere? Unaware Facial Recognition Using Brain-Computer Interfaces

Bellman

Martin

Liscano

et al. 2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

View full text Add to dashboard Cite

show abstract

Brain Computer Interface applications and classification techniques

Cited by 3 publications

References 15 publications

The Promise of Deep Learning for BCIs: Classification of Motor Imagery EEG using Convolutional Neural Network

The Promise of Deep Learning for BCIs: Classification of Motor Imagery EEG using Convolutional Neural Network

Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users

Excuse Me, Do I Know You From Somewhere? Unaware Facial Recognition Using Brain-Computer Interfaces

Contact Info

Product

Resources

About