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. 2013 Aug 14;13(8):10561-83.
doi: 10.3390/s130810561.

Gyroscope-driven Mouse Pointer With an EMOTIV® EEG Headset and Data Analysis Based on Empirical Mode Decomposition

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Free PMC article

Gyroscope-driven Mouse Pointer With an EMOTIV® EEG Headset and Data Analysis Based on Empirical Mode Decomposition

Gerardo Rosas-Cholula et al. Sensors (Basel). .
Free PMC article

Abstract

This paper presents a project on the development of a cursor control emulating the typical operations of a computer-mouse, using gyroscope and eye-blinking electromyographic signals which are obtained through a commercial 16-electrode wireless headset, recently released by Emotiv. The cursor position is controlled using information from a gyroscope included in the headset. The clicks are generated through the user's blinking with an adequate detection procedure based on the spectral-like technique called Empirical Mode Decomposition (EMD). EMD is proposed as a simple and quick computational tool, yet effective, aimed to artifact reduction from head movements as well as a method to detect blinking signals for mouse control. Kalman filter is used as state estimator for mouse position control and jitter removal. The detection rate obtained in average was 94.9%. Experimental setup and some obtained results are presented.

Figures

Figure 1.
Figure 1.
(a) Original EEG signal, (b) first five IMFs.
Figure 2.
Figure 2.
International system 10–20.
Figure 3.
Figure 3.
Proposed scheme, blinking detection and gyroscope processing system.
Figure 4.
Figure 4.
Head movement noise during double blinking events.
Figure 5.
Figure 5.
Preprocessing to reduce head movement noise.
Figure 6.
Figure 6.
EMD decomposition from four different electrodes near AF3. (a) FC5, (b) FC6, (c) P8, and (d) P7.
Figure 7.
Figure 7.
Noise reduction based on correlation function removing, (a) 1 IMF, (b) 2 IMFs, (c) 3 IMFs and (d) 4 IMFs.
Figure 8.
Figure 8.
Double blinking detection with noise reduction.
Figure 9.
Figure 9.
Gyroscope data and velocity target movement from subject head movement; target movement (red line), head movement (blue line).
Figure 10.
Figure 10.
Gyroscope data and velocity target movement from four different subjects head movements.
Figure 11.
Figure 11.
Simplified flow diagram for Kalman filter.
Figure 12.
Figure 12.
Kalman filtering as state estimator in mouse control and jitter removal; target movement (black line), observed movement (blue line), and filtered movement (red line).
Figure 13.
Figure 13.
General scheme of detection system proposed.
Figure 14.
Figure 14.
Typical EMD decompositions (Right) for blinking events (Left) from two different subjects under test.
Figure 15.
Figure 15.
Experimental setup of EEG-based mouse emulation.
Figure 16.
Figure 16.
Testing setup system.
Figure 17.
Figure 17.
Range of double blink detection for the classification module.
Figure 18.
Figure 18.
Average ROC curves obtained through measurements from AF4 (blue) and AF3 (red) electrodes.
Figure 19.
Figure 19.
Average ROC curves obtained for EMD decomposition from AF4 (blue) and for Wavelet decomposition (red) from the same electrode.

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