1. System control based on EEG signals
Dariusz Grabowski, Marcin Rdest
Silesian University of Technology, Electrical Eng. Faculty
Streszczenie: W artykule przedstawiono wyniki badan dotyczacych wystepowania asymetrii sygnalu EEG w zaleznosci od stanu emocjonalnego czlowieka. Wystepowanie takiej asymetrii umozliwiloby proste sterowanie
dwustanowe, stanowiac element interfejsu pomiedzy czlowiekiem a maszyna (BCI – Brain Computer Interface). Przeprowadzone eksperymenty potwierdzaja wystepowanie asymetrii, jednak róznice wyników pomiedzy
badanymi obiektami wymagaja zastosowania indywidualnych regul klasyfikacyjnych dla kazdego z nich.
Summary: Results of investigations concerning EEG signal asymmetry with respect to human emotional state have been presented in the paper. This phenomenon could make possible simple binary control and be an
element of brain computer interface (BCI). Experiments carried out within the scope of this work have confirmed the asymmetry existence but the difference in results between objects requires development and application of
individual classification rules for each object.
The forehead asymmetry for one of the objects inspected during the experiment:
positive stimuli on the left and negative one on the right
E l , sb − E r , sb • Θ1 (4-6 Hz), Θ2 (6-8 Hz),
=
e1, e 2 • α1 (8-10 Hz), α2 (10-13.5 Hz),
R
E l , sb + E r , sb
sb • β1 (13.5-20 Hz), β2 (20-30 Hz).
IAPS data base
The average value of the asymmetry factor The average deference between rising and falling times The average value of the asymmetry factor The average deference between rising and falling times
Data for electrodes F3-F4 (red bars - negative stimuli, blue bars – positive stimuli) Data for electrodes O1-O2 (red bars - negative stimuli, blue bars – positive stimuli)
Object 1 2 3 4 5 6 Average Object 1 2 3 4 5 6 Average
76% 65%
Success rate 75% 83% 67% 83% 75% 75% Success rate 83% 58% 75% 58% 58% 58%
No. of
Start samples Feature 1
nbc nbc
nb
ζ = ∑ ζ b ζ b = −∑ log 2 Class 1
c nb nb
b nt
Read EEG data Class 2
ζb – entropy for the branch b of the tree, Feature 2 Feature 3
Positive
Preprocessing:
ζ – average entropy,
artifact removal, filtering
nb – the number of instances in branch b,
nbc – the number of instances in branch b of class c, Feature
value
nt – the total number of instances in all branches.
STFT
Threshold 1 Threshold 2
Positive Negative Negative Positive
Feature extraction
Classification
The differences in asymmetry factors for positive and negative stimuli are larger for the F3-F4 electrodes. In that case the average success
Control information rate was about 76% (min 67%, max 83% ) while for the electrodes O1-O2 it was equal to 65%. Unfortunately, in both cases building individual
decision trees for each object was necessary.
Positive and negative emotions are the cause of EEG asymmetry signal but, at least for the feature set applied in the paper, for each object it
Stop could be seen within different subbands. As a consequence individual classification rules are required. It is interesting that the same kind of
emotions causes activation of the left hemisphere for some objects and the right one for the others. This reaction reminds the difference
between the left-handed and right-handed people.