Wink based facial expression classification using machine learning approach

Abstract

Facial expression may establish communication between physically disabled people and assistive devices. Different types of facial expression including eye wink, smile, eye blink, looking up and looking down can be extracted from the brain signal. In this study, the possibility of controlling assistive devices using the individual's wink has been investigated. Brain signals from the five subjects have been captured to recognize the left wink, right wink, and no wink. The brain signals have been captured using Emotiv Insight which consists of five channels. Fast Fourier transform and the sample range have been computed to extract the features. The extracted features have been classified with the help of different machine learning algorithms. Here, support vector machine (SVM), linear discriminant analysis (LDA) and K-nearest neighbor (K-NN) have been employed to classify the features sets. The performance of the classifier in terms of accuracy, confusion matrix, true positive and false positive rate and the area under curve (AUC)-receiver operating characteristics (ROC) have been evaluated. In the case of sample range, the highest training and testing accuracies are 98.9% and 96.7% respectively which have been achieved by two classifiers namely, SVM and K-NN. The achieved results indicate that the person's wink can be utilized in controlling assistive devices.

The classification of EEG-based winking signals: a transfer learning and random forest pipeline

“…It is apparent that the conversion of signals via CWT could provide meaningful features to be extracted through the transfer learning approach, which was also demonstrated through the present study. It is also worth noting that with regards to the classification of wink-based EEG signals, the present study has shown that exceptional classification was achieved via the proposed approach and was shown to be better than that of results reported by Rashid et al (2020) .…”

“…The result showed that the GRBF classifier performed well based on the extracted time-domain features. Rashid et al (2020) investigated the classification of wink-based EEG signals. The features of the EEG signals were extracted through the Fast Fourier Transform (FFT) and sample range methods (Rashid et al, 2020).…”

“… Rashid et al (2020) investigated the classification of wink-based EEG signals. The features of the EEG signals were extracted through the Fast Fourier Transform (FFT) and sample range methods ( Rashid et al, 2020 ). The FFT algorithm was utilised to transform the EEG signals into frequency domain features.…”

Five-Class SSVEP Response Detection using Common-Spatial Pattern (CSP)-SVM Approach

“…Due to neuromuscular disorders including amyotrophic lateral sclerosis (ALS) and locked-in syndrome, the individuals' motor functions are lost [2]. In those instances, the individual cannot communicate with others utilizing any mode of intelligence or expression [3]. To come up with a clarification, researchers are attempting to discover a wide range of assistive appliances.…”

“…The idea of BCI is extensively studying by researchers among those assistive appliances. In every BCI technology, the particular cognitive task has been interpreted into device command which can be utilized in the handling of assistive appliances [4] [3]. Brain-operated wheelchair, domestic equipment controlling, robotic arm commanding, spelling technology, workload recognition, and authentication detection system are the widely adopted BCI applications [5] [6].…”