Numerous investigations have been conducted to enhance the motor imagery-based brain–computer interface (BCI) classification performance on various aspects. However, there are limited studies comparing their proposed feature selection framework performance on both objective and subjective datasets. Therefore, this study aims to provide a novel framework that combines spatial filters at various frequency bands with double-layered feature selection and evaluates it on published and self-acquired datasets. Electroencephalography (EEG) data are preprocessed and decomposed into multiple frequency sub-bands, whose features are then extracted, calculated, and ranked based on Fisher’s ratio and minimum-redundancy-maximum-relevance (mRmR) algorithm. Informative filter banks are chosen for optimal classification by linear discriminative analysis (LDA). The results of the study, firstly, show that the proposed method is comparable to other conventional methods through accuracy and F1-score. The study also found that hand vs. feet classification is more discriminable than left vs. right hand (4–10% difference). Lastly, the performance of the filter banks common spatial pattern (FBCSP, without feature selection) algorithm is found to be significantly lower (p = 0.0029, p = 0.0015, and p = 0.0008) compared to that of the proposed method when applied to small-sized data.

In light of the negative effects of mental stress on health, there is growing scientific attention placed on understanding it better. This study aims to introduce a method that uses biological indicators such as skin surface temperature and skin resistance using easily accessible data to detect and predict stress levels. The publicly available dataset used in this study included the physiological signals of a group of university students whose signals were recorded by a wearable device during three exam sessions (two midterm and one final exam). Features were extracted from heart rate, electrodermal activity, and physiological and skin surface temperature and were input for the classification. In the labeling process, the stress levels were classified as high, low, or moderate. Machine learning algorithms such as Support Vector Machines (SVM) and K-Nearest Neighbors (KNN) with classifiers were trained and tested with the labeled data The stress level of a subject was predicted with the highest F1 score of up to 0.8 and accuracy of higher than 70%. Such findings highlight the important role of skin conductance and surface temperature in estimating stress levels.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects children and adolescents. It is characterized by symptoms of inattention, hyperactivity, and impulsivity. Currently, the diagnosis of patients with ADHD primarily relies on behavioral observations combined with educational and psychological assessments. This makes early detection and timely diagnosis of ADHD more challenging. However, recent findings have shown that electroencephalography (EEG) signals can provide valuable insights into cognitive abilities and have demonstrated promising potential in early ADHD diagnosis. In this study, EEG signals were utilized in conjunction with several machine learning algorithms to evaluate the effectiveness of these algorithms and their potential for improving the early detection of ADHD. A public EEG dataset of ADHD patients from an open-access database has been used. At first, the preprocessing is carried out through some data wrangling techniques. The next step involves feature selection, where each frequency channel band is considered a feature identified. Finally, the classification will be performed using Support Vector Machine (SVM) models and Linear Discriminant Analysis (LDA) algorithm, to detect ADHD. The results showed that the SVM -based classification model provided more accurate and reliable results (with 96.42% accuracy, 95.77% sensitivity, 96.88% specificity) than the LDA-based model (with 63.92%accuracy, 25.37% sensitivity, 91.33% specificity). These findings will inform future research as the authors seek to develop an effective and accurate tool for early diagnosis of ADHD. Such a tool could help families and healthcare professionals intervene and treat the disorder in a timely manner, leading to better outcomes, especially for children.