Computational Intelligence - August 2016 - 60
where N is the number of possible targets
(number of visual stimuli in this study), A
is the probability that the target was accurately classified (accuracy here), and T is
the duration of the trial in minutes.
Task Completion Time (seconds): Task
completion time measures the time
duration needed to perform one trial.
condition out of 6 conditions (3 collaboration modes # 2 luminance contrasts)
when finished after the experiment.
E. Data Acquisition and
Signal Processing
P = lim 1
T " ∞ 2T
8 x^ t h2 dt (4)
-T
T
where x (t) is the amplitude of the EEG signal and T is the duration of one cycle [46].
3) User Evaluations
User Preference: Participants were asked
to choose their favorite experiment
FPZ
FP1
AF7
F7
FT7
T9
T7
AF3
F5
F3
F1
FZ
AF8
AF4
AFZ
F2
FC1
FCZ
FC2
FC4
C5
C3
C1
CZ
C2
C4
CP5
CP3
CP1
CPZ
CP2
CP4
P5
PO7
P3
P1
PO3
O1
PZ
POZ
OZ
IZ
Figure 3 Electrode Montage.
IEEE Computational intelligence magazine | august 2016
P2
P4
PO4
O2
F8
F6
F4
FC3
P7
60
FP2
FC5
TP7
2) Signal Processing
Many methods have been used in the
SSVEP signal classification, such as Harmonic Sum Decision (HSD) [16], [48],
Canonical Correlation Analysis (CCA)
[30], Linear Discriminant Analysis [49],
and Minimum Energy Combination
[31]. HSD was chosen in the current
study for its efficiency and simplicity.
Data processing included two phases:
preprocessing and classification. In the
preprocessing phase, raw EEG data was
filtered and transformed into the frequency domain. In the classification
phase, the brain signal was classified and
1) Data Acquisition
SSVEP brain waves were recorded using
an EEG cap embedded with 16 channels
(Fz, C3, Cz, C4, CPz, P7, P3, Pz, P4, P8, PO3,
POz, PO4, O1, Oz, and O2) based on the
modified 10-20 system of the International Federation [47]. Brain signals from
channel O1 and O2 were used as control
signal. Figure 3 showed the electrode
montage used in the study. Recordings
were referenced to the left mastoid and
grounded to location AFz [42].
SSVEP brain waves were amplified
with a g.USBamp amplifier (g.tec
2) Brain Activity
Spectral Power (mv 2 ): Spectral Power is
defined as,
Medical Engineering). Data collection,
online signal processing and offline data
analysis were conducted using LabVIEW-based software developed by BCI
Lab at North Carolina State University.
FT8
FC6
C6
CP6
P6
PO8
T8
TP8
P8
T10
Table of Contents for the Digital Edition of Computational Intelligence - August 2016
Computational Intelligence - August 2016 - Cover1
Computational Intelligence - August 2016 - Cover2
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