EEG

Romeda / EEG

Brain Computer Interface with Low Cost Commercial EEG Device

Gürkan Küçükyıldız, Suat Karakaya

In this study, a brain computer interface (BCI) system was explored.  Instead of high cost EEG devices, a low cost commercial EEG device (EMOTIV) was used. Raw EEG data was obtained by using research edition SDK of EMOTIV.  EMOTIV EEG device has 14 channels (10-20 placement) for EEG and two channels (x and y axis gyro: GYROX, GYROY) for head movements.  Head movements and eye-blink can affect the EEG data and are usually referred to as artifacts. In this study, raw EEG data was pre-processed using the x and the y axis gyro data and the two front EEG channels, namely AF4, F8, in order to determine whether the data is artifact free or not. EEG data was collected from subjects that were asked to accomplish two cognitive tasks: pushing a cube and relaxing. Subjects performed each task for a duration of five seconds during 20 trials. The acquired EEG data was divided into 0.25 second epochs. Epochs that were determined to have artifacts were discarded. Power spectral density (PSD) and time domain based features were extracted from artifact free epochs. The features were then used to train a Support Vector Machine (SVM) to determine the corresponding task. The performance of the SVM classifier was compared to that of an Artifical Neural Network (ANN) based one. Experimental results show the efficacy of the SVM based scheme.

Romeda Blog - 04/25/2017

Design and Navigation of a Robotic Wheel Chair

Gürkan Küçükyıldız, Suat Karakaya

In this study, design and navigation of a robotic wheelchair for disabled or elderly people was explored. Developed system consists of a wheelchair, high-power motor controller card, Kinect camera, RGB camera, EMG sensor, EEG sensor, and computer.  Kinect camera was installed on the system in order to provide safe navigation of the system. Depth frames, captured by Kinect camera, were processed with developed image processing algorithm to detect obstacles around the wheelchair. RGB camera was mounted to system in order to detect head-movements of user. Head movement, has the highest priority for controlling of the system. If any head movement detected by the system, all other sensors were disabled. EMG sensor was selected as second controller of the system. Consumer grade an EMG sensor (Thalmic Labs) was used to obtain eight channels EMG data in real time. Four different hand movements: Fist, release, left and right were defined to control the system using EMG. EMG data was classified different classification algorithms( ANN,SVM and random forest) and most voted class was selected as result.  EMG based control can be activated or disabled by user making a fist or release during three seconds.  EEG based control has lowest priority for controlling the robotic wheelchair. A wireless 14 channels EEG sensor (Emotiv Epoch) was used to collect real time EEG data. Three different cognitive tasks: Solving mathematical problems, relaxing and social task were defined to control the system using EEG. If system could not detect a head movement or EMG signal, EEG based control is activated.   In order to other to control user should accomplish the relative cognitive task.   During experiments, all users could easily control the robotic wheelchair by head movements and EMG movements. Success of EEG based control of robotic wheelchair varies because of user experiments. Experienced users and  un-experienced user changes the result of the system.

Romeda Blog - 04/25/2017