Data_Sheet_1_Multiclass Classification Based on Combined Motor Imageries.pdf حسب Cecilia Lindig-León (7889777)

Timeline of a single trial for dataset 1. حسب Rabia Avais Khan (16955056)

"…Six different classification algorithms, namely support vector machine, linear discriminant analysis, k-nearest neighbor, naïve Bayes, decision trees, and logistic regression, have been compared to classify the EEG data accurately. …"

Block diagram of proposed methodology. حسب Rabia Avais Khan (16955056)

"…Six different classification algorithms, namely support vector machine, linear discriminant analysis, k-nearest neighbor, naïve Bayes, decision trees, and logistic regression, have been compared to classify the EEG data accurately. …"

Timeline of a single trial for dataset 2. حسب Rabia Avais Khan (16955056)

"…Six different classification algorithms, namely support vector machine, linear discriminant analysis, k-nearest neighbor, naïve Bayes, decision trees, and logistic regression, have been compared to classify the EEG data accurately. …"

iNCog-EEG (ideal vs. Noisy Cognitive EEG for Workload Assessment) Dataset حسب Fariya Bintay Shafi (21692408)

"…</p><h3>Applications</h3><p dir="ltr">This dataset can be applied to a wide range of research areas, including:</p><ul><li>EEG signal denoising and artifact rejection</li><li>Binary and hierarchical <b>cognitive workload classification</b></li><li>Development of <b>robust Brain–Computer Interfaces (BCIs)</b></li><li>Benchmarking algorithms under <b>ideal and noisy conditions</b></li><li>Multitasking and mental workload assessment in <b>real-world scenarios</b></li></ul><p dir="ltr">By combining controlled multitasking protocols with deliberately introduced environmental noise, <b>iNCog-EEG provides a comprehensive benchmark</b> for advancing EEG-based workload recognition systems in both clean and challenging conditions.…"