Integration of IMU Sensor on Low-Cost EEG
and Design of Cursor Control System with
ANFIS

Fatih Bozkurt; Ahmet Ça?da? Seçkin; Aysun Co?kun

doi:https://doi.org/10.14445/22315381/IJETT-V54P223

Research Article | Open Access | Download PDF

Volume 54 | Number 2 | Year 2017 | Article Id. IJETT-V54P223 | DOI : https://doi.org/10.14445/22315381/IJETT-V54P223

Integration of IMU Sensor on Low-Cost EEG and Design of Cursor Control System with ANFIS

Fatih Bozkurt, Ahmet Ça?da? Seçkin, Aysun Co?kun

Citation :

Fatih Bozkurt, Ahmet Ça?da? Seçkin, Aysun Co?kun, "Integration of IMU Sensor on Low-Cost EEG and Design of Cursor Control System with ANFIS," International Journal of Engineering Trends and Technology (IJETT), vol. 54, no. 2, pp. 162-169, 2017. Crossref, https://doi.org/10.14445/22315381/IJETT-V54P223

Abstract

In this paper, an Adaptive Neuro-Fuzzy System (ANFIS) is used for fusion of Electroencephalography (EEG) and Inertial Measurement Unit (IMU) sensors and it is aimed to perform cursor control with this system. The combination of EEG and IMU sensors is intended to perform cursor control by combining cognitive responses and head movements. The system consists of MPU6050 IMU and the NeuroSky Mindwave EEG device. With the prepared recording program for experimental setup, the received data are recorded as separate program tasks with time stamp. Simulator, IMU and EEG tasks prepared for data collection are prepared. During the data collection phase, a cursor simulator was first prepared and the subjects were asked to follow this cursor setup for five minutes. The simulator task records cursor position and velocity. With the IMU task, 3 axis acceleration and angular rotation are recorded. The data collected with the EEG are raw values and extracted feature values. In the ANFIS learning process, IMU features and EEG features were used as input and simulator data was used as output. As a result, ANFIS cursor control is provided with 5% error in cursor movement and 0.3% error for cursor click control.

Keywords

ANFIS; EEG; IMU; Human Computer Interaction; Signal Processing.

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