DECODING OF MOTOR TASK DIFFICULTY AND EXECUTION SPEED FROM EEG DATA WITH APPLICATION TO STROKE REHABILITATION

Sezen Yağmur Günay
Computer Science and Engineering, MSc. Thesis, 2016

Thesis Jury

Assoc. Prof. Müjdat Çetin (Thesis Advisor), Assoc. Prof. Volkan Patoğlu,

Asst. Prof. Hülya Yalçın

Date & Time: August 3rd, 2016 –  10.40 AM

Place: FENS L065

Keywords: electroencephalogram, brain-computer interfaces, event-related synchronization and desynchronization, robotic rehabilitation systems

Abstract

Brain-computer interfaces (BCIs) which provide an alternative channel between human brain and computer world are hope for many patients who suffer from neurological diseases such as ALS and stroke. Scientists benefit from BCIs not only for communication but also for rehabilitation and effective use of robotic exoskeletons. While healthy people can decide the speed level and the amount of force applied for daily activities, stroke patients are not able to transfer their intended movement speed and force magnitude to robotic limbs and mechanical devices. Even though brain signals potentially contain such information about task execution, existing BCI systems do not exploit that information. In this thesis, the possibility of decoding intended human activity speed and task difficulty levels from an electroencephalography (EEG) based BCI system is investigated. In particular, two experimental setups are designed to collect data while subjects are performing two different tasks, and different protocols are proposed with their advantages and drawbacks to extract accurate information from these setups. Moreover, the problem of intention level detection is analyzed in response to task difficulty and speed level. As it is not possible to access the true intention level of a human being to execute a particular task, we use the difficulty and speed of the executed task as proxies for the true intention level and then analyze the corresponding neural correlates. We have applied several classification protocols and our results indicate that some classification protocols are able to detect task speed and difficulty from EEG signals.