Pictorial Task Assistance System using Electroencephalography Signals

  • Sadaf Afreen University of Engineering and Technology, Taxila, Pakistan
  • Sanay Muhammad Umar Saeed University of Engineering and Technology, Taxila, Pakistan
  • Sheharyar Khan University of Engineering and Technology, Taxila, Pakistan
Keywords: Pictorial Speller BCI, Electroencephalography (EEG), Motor Neuron Disease (MND), K-Nearest Neighbor


Neuromuscular disorders are a significant health problem globally. Patients may experience paralysis, muscle weakness, and communication problems because of these disorders. We propose a Pictorial Task Assistance System to help patients with communication issues using Electroencephalography (EEG). We developed an interface for patients containing an image of food and water. We collected EEG data from 25 healthy students using the Muse headset and Muse monitor app for our study, while they selected one of the images. The EEG data was used to train three supervised machine-learning algorithms for classification. The labels were acquired manually from participants. Using 10-fold cross-validation the results demonstrated that the Random Forest (RF) classifier achieved 88% accuracy, K-Nearest Neighbors (KNN) 80%, and 76% accuracy in Logistic Regression (LR) in the classification of food and water images. These results suggest that the proposed system has the potential to be a useful tool for patients suffering from neuromuscular disorders to perform communication for their necessary tasks.