This study compared the online effect of using a broad frequency band (8-26Hz) versus a narrow band (centered at 12Hz) for a motor imagery-based brain-computer interface (BCI). Six subjects performed cursor tasks using both bands. There was no significant difference in accuracy or invalid rates between the bands. However, subjects with high mu rhythm power performed better with the narrow band, while subjects with high beta power performed better with the broad band. Therefore, the broad band is preferred to include both mu and beta rhythms and achieve better performance in most subjects. Future work with more subjects is needed to confirm these findings.

1. Compare the Online Effect of the Wide and Narrow Frequency
Band on Motor Imagery based Brain-Computer Interface
Shuying Zhang1, Jianjun Meng1, and Bin He1,2
Department of Biomedical Engineering1, Institute for Engineering and Medicine2, University of Minnesota
Biomedical Functional Imaging and
Neuroengineering Laboratory
Introduction
Brain-Computer Interfaces (BCIs) have been shown to assist people with
impairments to interact with devices such as a prosthetic arm [1]. Recently,
noninvasive BCI using electroencephalogram (EEG) based on motor imagery method
was shown to control a virtual cursor and a virtual helicopter [2]-[3].
Human voluntary movement is associated with two power changes: event-
related desynchronization (ERD) and event-related synchronization (ERS). ERD is the
power decrease occurring right before the movement and sustains with the
movement. ERS is the power increase occurring after the end the movement. The
ERD is commonly seen in the Mu rhythm (8-12Hz), which is closely associated with
imagination of hand movements [4]. In addition, ERD is also seen in the beta rhythm
(12-30 Hz), which is shown to be correlated with BCI motor imagery as well [5].
Currently, most MI-based BCI studies focus on the ERD using two frequency
band widths: a broad band from 8-30 Hz that includes Mu and Beta rhythm; a
narrow band that includes only the Mu rhythm. There is a lack of consensus on
whether or not MI-based BCI has a better performance using broad frequency band
or narrow frequency band.
In order to examine how the band width affects the BCI performance, on-line
analysis using BCI2000 1-D cursor task was conducted to (1) compare which band
width gives a better BCI performance (2) optimize the classification algorithm for BCI
motor imagery.
Methods
Experimental Subjects
Six healthy human subjects with previous BCI experience participated in this
study (two females and four males, aged 20-29 years), who all gave written
consent according to a protocol approved by the Institutional Review Board of the
University of Minnesota.
Experimental Paradigm
Participants sat in a comfortable chair facing a computer monitor to conduct
a motor imagery cursor task using BCI2000 interface. Each Subject performed one
narrow band (centered on 12Hz, 3Hz bin width) session and one broad band (cover
8 – 26Hz, including both mu and beta rhythm) session in a random order. Each
session has 6 to 8 five-minute runs. Each run has 25 trials with a target appearing
either on the left or the right side of the screen. If the cursor hits the target, the
trial is recorded as a “hit”. If the cursor hits the opposite target, it is recorded as a
“miss”. If the cursor does not hit either one, it is recorded as an “invalid”.
Data Acquisition
64-channel EEG was acquired using a Neuroscan System. EEG signals were
filtered from 0.1 – 200 Hz and sampled at 1000 Hz by a Neuroscan amplifier and
imported to BCI2000 [6] with large Laplacian filtering to improve the signal-to-
noise ratio (Figure 1). The signal feature was extracted using an autoregressive
model to calculate voltage spectra of channel C3 and C4. The resulting feature
provided feedback that drives the movement of the cursor.
Figure 2. Subject’s view of BCI2000 1-D cursor task. When the target (yellow
bar) appears on the left, the subject imagined moving the left hand. When
the target is on the right, the subject imagined moving the right hand. The
cursor moved left or right according to power change of C3 and C4.
Figure 1. EEG Channel layout with relevant channels in feature extraction. C3 and
C4 were used for extracting the signal features during hand movement imagination.
For all broad and narrow band sessions, two large Laplacian filters were used to
calculate the autoregression (AR) based spectrum around C3 and C4 in order to
determine the cursor movement. In each large Laplacian filter, the power of C3 was
subtracted from F3, Cz, P3 and T7; the power of C4 was subtracted from F4, Cz, P4
and T8.
0.00%
20.00%
40.00%
60.00%
80.00%
100.00%
120.00%
1 2 3 4 5 6
PTC
subject #
broad
band
narrow
band
Figure 3. On-line performance of broad band and narrow band methods. Percent total correct (PTC)
accuracy was calculated by dividing the number of hits by the number of total outcomes (including
invalids). The accuracy has no significant difference (two sample t-test, P = 0.76 > 0.05). Percent total invalid
(PTI) was calculated by dividing the number of invalids by the total number of total outcomes. The PTI has no
significant difference ( two sample t-test, a P = 0.57 > 0.05. )
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
1 2 3 4 5 6
PTI
subject #
broad
band
narrow
band
There is no significant difference of PTC and PTI
values between broad band and narrow band methods.
Individual differences of the sensorimotor control exist
between subjects. Some subjects exhibited high power
in mu rhythm; while others showed high power in beta
rhythm. Subjects who had high power within beta
rhythm but not within mu rhythm had better BCI
performance using the broad band method. Subjects
who had high power within mu rhythm but not within
beta rhythm had better performance using the narrow
band method.
In order to ensure a better performance in most
subjects, broad band method is preferred since both
mu rhythm and beta rhythm are included.
In future experiments, more subjects can be used to
confirm the hypothesis.
Left Hand Right Hand
Subject1Subject3
12Hz 24Hz
12Hz
12Hz
12Hz24Hz
24Hz
24Hz
Figure 4. Topographies of two subjects who achieved similar results in broad-band and narrow-band
methods. Subject 1 has high activity at 12Hz but not 24Hz. Subject 3 has high activities at both 12Hz
and 24Hz.
This material is based upon work supported by the National Science
Foundation IGERT under DGE-1069104. This work was also
supported in part by ONR N000141110690, and NSF CBET-
0933067.
[1]. Collinger, Jennifer L., et al. "Collaborative Approach in the Development of High‐Performance Brain–
Computer Interfaces for a Neuroprosthetic Arm: Translation from Animal Models to Human Control." Clinical
and translational science 7.1 (2014): 52-59.
[2]. Royer, Audrey S., et al. "EEG control of a virtual helicopter in 3-dimensional space using intelligent control
strategies." Neural Systems and Rehabilitation Engineering, IEEE Transactions on 18.6 (2010): 581-589.
[3]. Doud, Alexander J., et al. "Continuous three-dimensional control of a virtual helicopter using a motor
imagery based brain-computer interface." PloS one6.10 (2011): e26322.
[4]. Pfurtscheller, G., et al. "Mu rhythm (de) synchronization and EEG single-trial classification of different
motor imagery tasks." Neuroimage 31.1 (2006): 153-159.
[5]. Bai, Ou, et al. "A high performance sensorimotor beta rhythm-based brain–computer interface associated
with human natural motor behavior." Journal of neural engineering 5.1 (2008): 24.
[6]. Schalk, Gerwin, et al. "BCI2000: a general-purpose brain-computer interface (BCI) system." Biomedical
Engineering, IEEE Transactions on 51.6 (2004): 1034-1043.
Subject 1
BroadBandNarrowBand
Figure 5. C3 and C4 channel power spectrum of two subjects. Subject 1 has a stronger power at 12Hz than 24Hz
using both broad band and narrow band method. Subject 5 has a stronger power at 24Hz than 12Hz in both broad
band and narrow band. Subject 1 has a better on-line performance using the narrow band method. Subject 5 has a
better on-line performance using the broad band method.
Subject 5
BroadBandNarrowBand