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

Abstract

Brain Computer-Interface (BCI) technology plays a considerable role in the control of rehabilitation or peripheral devices for stroke patients. This is particularly due to their inability to control such devices from their inherent physical limitations after such an attack. More often than not, the control of such devices exploits electroencephalogram (EEG) signals. Nonetheless, it is worth noting that the extraction of the features and the classification of the signals is non-trivial for a successful BCI system. The use of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features. However, the employment of such a method towards BCI applications, particularly in regard to EEG signals, are somewhat limited. The present study aims to evaluate the effectiveness of different TL models in extracting features for the classification of wink-based EEG signals. The extracted features are classified by means of fine-tuned Random Forest (RF) classifier. The raw EEG signals are transformed into a scalogram image via Continuous Wavelet Transform (CWT) before it was fed into the TL models, namely InceptionV3, Inception ResNetV2, Xception and MobileNet. The dataset was divided into training, validation, and test datasets, respectively, via a stratified ratio of 60:20:20. The hyperparameters of the RF models were optimised through the grid search approach, in which the five-fold cross-validation technique was adopted. The optimised RF classifier performance was compared with the conventional TL-based CNN classifier performance. It was demonstrated from the study that the best TL model identified is the Inception ResNetV2 along with an optimised RF pipeline, as it was able to yield a classification accuracy of 100% on both the training and validation dataset. Therefore, it could be established from the study that a comparable classification efficacy is attainable via the Inception ResNetV2 with an optimised RF pipeline. It is envisaged that the implementation of the proposed architecture to a BCI system would potentially facilitate post-stroke patients to lead a better life quality.

The classification of skateboarding tricks via transfer learning pipelines

“…The present study has demonstrated that through the proposed pipeline, a better classification accuracy could be achieved as compared to the conventional means reported in the literature, particularly with regards to the classification of skateboarding tricks. The encouraging results reported suggests that the proposed pipeline could be beneficial in providing an objective-based judgment in The findings of the present investigation are in agreement with other studies that have employed such a technique in different applications, for instance, Lee, Yoon and Cho (2017) , Rangasamy et al (2020) as well as Mahendra Kumar et al (2021) . Nonetheless, it is worth noting that the efficacy of the pipelines is highly dependent on the dataset utilized, and the performance may vary.…”

“…This study exploits the use of three families of pre-trained CNN models, i.e ., the MobileNet, NasNet and ResNet families. The rationale of employing transfer learning (TL) models is to reduce the model development time as the CNN models are not required to be built from scratch ( Amanpour & Erfanian, 2013 ; Chronopoulou, Baziotis & Potamianos, 2019 ; Mahendra Kumar et al, 2021 ). A departure from conventional means of using such models is that the present study replaces the fully connected layers that is often referred to dense layers with a conventional machine learning model, i.e ., SVM.…”

Detection of Obesity Stages Using Machine Learning Algorithms

“…Then, when growing a tree, only a random subset of all attributes is considered at each node, calculating the best split for that subset instead of always calculating the best possible split for each node. Thus, simplifying the optimization procedure, random trees use this split selection to create a reasonably balanced tree where a single global setting for the ridge value works across all leaves [26].…”

ConvNets for Electroencephalographic Decoding of Attempted Arm and Hand Movements of People with Spinal Cord Injury

“…We selected the Morlet mother wavelet for this purpose, as it has been extensively used in biomedical signal processing. [17,27,28] We set the frequency limits to range between 0.5 and 100 Hz. The resulting scalograms were concatenated along their horizontal axis and saved as normalized grayscale images.…”