SELF-MOVEMENT, OBSERVATION, AND IMAGINATION EFFECTS ON Mu RHYTHM AND READINESS POTENTIALS (RPs): TOWARDS A BRAIN-COMPUTER INTERFACE (BCI)
J. A. Pineda, B.Z. Allison,
and A. Vankov
Department of Cognitive
Science
University of California,
San Diego
La Jolla, CA 92093
Please send correspondence to:
Jaime A. Pineda, Ph.D.
Department of Cognitive Science 0515
University of California, San Diego
La Jolla, CA 92093
(858) 534-7087 (office)
ABSTRACT
Current movement-based BCIs utilize spontaneous EEG rhythms associated
with movement, such as the mu rhythm, or responses time-locked to movements
that are averaged across multiple trials, such as the Readiness Potential (RP)
as control signals. In one study, we
report that the mu rhythm is not only modulated by the expression of
self-generated movement but also by the observation and imagination of
movement. In another study, we show
that simultaneous self-generated multiple limb movements exhibit properties distinct from those of single
limb movements. Identification and
classification of these signals with pattern recognition techniques provides
the basis for the development of a practical BCI.
INTRODUCTION
The concept of a direct interface between the human brain and a sophisticated artificial system, such as a computer, is not a new one. In recent years, there have been advances in a number of fields that make the design and development of a practical brain computer interface (BCI) possible. Such a BCI would be capable of quickly and reliably extracting meaningful information from the human electroencephalogram (EEG) or other recordable electrical potentials. Over the past decade, several working BCI systems have been described in the literature [2, 3, 6, 7, 8]. These systems use a variety of collection mechanisms, pattern recognition approaches, and interfaces, and require different types of cognitive activity on the part of the user.
One type of BCI that has been examined extensively
derives information from a user’s movements or the imagination of
movement. Many of these movement-based BCIs can recognize
changes in the human mu rhythm, which is an EEG oscillation recorded in the
8-13 Hz range from the central region of the scalp overlying the sensorimotor
cortices [4]. This rhythm is large when
a subject is at rest, and is known to be blocked or attenuated by
self-generated movement. Indeed, the mu
wave is hypothesized to represent an “idling” rhythm of motor cortex that is interrupted
when movement occurs. The free running EEG shows characteristic changes in mu activity, which are unique
for the movement of different limbs. These findings have and will continue to
be useful in the construction of BCI systems.
The imagination or performance of a movement is also
generally accompanied by a readiness potential (RP; also called
Bereitshaftspotential or BP) which is most prevalent over cortical motor
areas. The RP is a time-locked response
to the movement event, or event related potential (ERP), that is extracted from
the ongoing EEG using signal averaging techniques across a number of trials.
The primary goal of the two studies we report was to
characterize mu and RP signals in simple, straightforward tasks. The recognition and discrimination of these
signals could then provide a basis for the development of a practical BCI, one
that would be useful to both normal and disabled individuals.
STUDY 1
In
this study, we show that the mu rhythm is attenuated not only by self-generated
movement but also when a subject observes
the movement or imagines making the
same, self-generated movement.
According to Rizzolatti and colleagues, the responsiveness of the mu
wave to visual input may be the human electrophysiologic analog of a population
of neurons in area F5 of the monkey premotor cortex [1,5]. These cells respond both when the monkey
performs an action and when the monkey observes a similar action made by
another monkey or by an experimenter.
Other, older studies have reported that
mu-like waves are blocked by thinking about moving. For example, individuals with amputated
limbs can block this rhythm by thinking about moving the amputated limb. The blocking of the mu rhythm by visual and
imagery input may have implications for understanding movement-related
responses and for the rehabilitation of movement-related neurological
conditions.
METHODS
Subjects
in this study were 17 healthy volunteers (10 men, 7 women, ranging in age
between 19-58, with a mean of 27.7 years).
Most subjects were students or employees at the University of
California, San Diego (UCSD) and naive to the purposes of the experiment. Only 10 subjects were used for statistical
analysis because of problems with noise.
All subjects signed a consent form that was approved by the UCSD Human
Subjects IRB committee.
EEG
signals were recorded from 6 sites on an electrode cap placed over frontal (F3,
F4), central (C3, C4), and occipital (O1, O2) areas according to the standard
10-20 International Electrode Placement System. Blinks and eye movements were monitored with an electrode in the
bony orbit dorsolateral to the right eye.
EEG was amplified by a Grass model 7D polygraph using 7P5B pre-amplifiers
with bandpass of 1-35 Hz. For
computerized data collection and analysis, the ADAPT ( ã A. Vankov, 1997) scientific software was
used. EEG was digitized on-line for
two minutes at a sampling rate of 256 Hz.
All electrode sites showed impedance of less than 5 kOhms.
Subjects
participated in four conditions: 1) rest:
in which subjects sat in a comfortable chair inside and acoustic chamber, but
no particular task was required; 2) self-generated
movement: subjects were asked to move their opposing thumb to middle
fingers of the right hand (making a “duck” movement); 3) observation: subjects watched a confederate of the experimenter
perform the “duck” movement; and 4) imagination:
subjects were instructed to imagine performing the self-generated “duck”
movement without actually doing it. The
confederate faced the subject who was seated approximately four feet away
throughout all conditions of the experiment.
The power spectrum was calculated for each second of the EEG, and mean
power within the mu range (8-13 Hz) was calculated for each condition over two
minutes.
RESULTS
During the rest condition, subjects exhibited significant power in the
8-13 Hz frequency range. This rhythm
showed statistically significant changes during the various conditions
(F(3,27)=4.98, P<0.01). Pairwise
comparisons showed that the main differences were a reduction in power during
the self-generated movement and the observation conditions (see Fig. 1). Post-hoc analysis of the data showed that
during the imagination condition, mu power decreased at frontal sites but was
less affected at central and occipital sites (site x condition,
(F(15,135)=2.22, P< 0.01).
STUDY 2
Numerous studies have explored the
RPs and mu changes associated with single movements of the finger and hand.
However, the electrophysiology of left and right foot movement, or those
preceding the voluntary simultaneous movement of multiple limbs, has not been
thoroughly explored. This information is necessary to better understand how the
brain’s activity gives rise to different movements, and also expands the range
of input signals that could be used in a BCI.
This study recorded EEGs from human subjects performing voluntary movements of either one or two limbs at self-paced intervals. Results confirmed that each type of movement is associated with unique EEG characteristics that were classified using Thoughtform Interpretation Studio (c) software.
METHODS
A
total of 18 subjects (mean age 23.7 +/- 2.8 years) were run in this experiment.
Seven subjects were female, with 3 of the female and two of the male subjects
being left handed. Most were undergraduate students at UCSD who were
compensated with either class credit or monetary payment. All subjects were
native English speakers, with no sensory or motor deficits and no history of
psychological disorders.
EEG
activity was recorded using an electrode cap with monopolar Ag/AgCl electrodes
overlying nine cortical sites: F3, Fz, F4, C3, Cz, C4, P3, Pz, and P4
(according to the standard 10-20 International Electrode Placement System)
referenced to linked mastoids. Signals were amplified 10,000 times and recorded
with a bandpass between .1-100 Hz. EOG activity was recorded through an
electrode placed over the right orbital bone. Eye activity was amplified 5,000
times and with a bandpass of .3-100 Hz.
All electrode sites had an impedance of less than 5 kOhms. Subjects’ hand
movements were detected through two joysticks, while a foot pedal device
recorded foot movements. All data, including subjects’ movements and EEG, were
sampled at 256 Hz and recorded on a computer for later analysis.
In
single movement trials, subjects made voluntary movements of either left or
right hand or left or right foot during a 10 minute-long trial block. The
movements could be of any limb and there was at least a five-second delay
between each movement. Subjects were
instructed not to worry about randomizing which limb was moved or ensuring a
fair distribution of different limb movements. Instructions were identical for
multiple movement trials except that they were asked to move any two limbs
simultaneously.
Thoughtform
Interpretation Studio (TIS 2.0) is a commercially available product that makes
EEG pattern recognition and single trial event detection possible. During the learning mode, input signals are
decomposed into features on a multidimensional phase space using a variety of
techniques, including time-frequency expansion, feature coherence analysis, and
principal component analysis (PCA).
This is followed by a state discriminant analysis to find feature
clusters that are most reliably different between two epoch types. The resulting Interpretation Maps are then
used in the interpretation mode to classify new single trial data. We used the single-limb RP averaged data
collected in this study as input to TIS to determine whether trial data for
left/right-hand and left/right-foot movements could be easily
discriminated.
RESULTS
The ERP data obtained show that the RPs preceding 2 types of combined movement (left foot/right hand movement and right foot/left hand movement) have a significantly larger peak amplitude than any other single or combined movement (see Fig. 2). In addition, each of the four single movement types shows unique RP and mu rhythm characteristics. TIS classification of averaged data resulted in reliable discrimination of movement categories with 60-100% confidence. We are currently examining the results of single trial analyses.
Our initial attempts to
elicit reliable mu and RP signals by self-generated movement, observation of
movement, and by the imagination of movement have been successful. Furthermore, attempts to discriminate such
signals at the single trial level have also been encouraging. Future work will involve detecting RPs to
imagined single and multiple movements and comparing those to RPs evoked by
overt movements. We also intend to
evaluate other preprocessing approaches, such as Independent Component Analysis
(ICA), as well as neural networks for the classification of signals.
LITERATURE CITED
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Fig. 1. Left panel represents twenty seconds of EEG data for a single
subject during the REST, SELF-GENERATED MOVEMENT (opposing thumb to middle two
fingers or “duck” movement), OBSERVATION, and IMAGINATION conditions. Center panels represent the integrated power
for the entire 120 seconds, expressed in mV2/sec. Panels on the right depict both time and
frequency information. For each of the
120 seconds, the integrated power in the 8-13 Hz range (|FFT[VC3(t)
– VC4(t)](w)|2dw) is shown in mV2/sec so as to be comparable to the scale in
center panels.
Fig. 2. Grand average ERPs recorded at the vertex site (Cz) during the
second preceding voluntary movement combinations of two limbs. The Readiness Potential (RP) is the increased
negativity that develops hundreds of milliseconds prior to the movement at time
zero. Note that the movements of
diagonally opposing limbs, shown in the center, produce larger RPs than other
movement combinations.