Sitting and Standing Intention Detection based on the Complexity of EEG
Signal
Wenwen Chang1, Wenchao Nie1, Yueting
Yuan1,Yuchan Zhang1 and Guanghui
Yan1
1 School of Electronic and Information
Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Email: wenwenchang@emailaddress.com.
Based on the brain signals, decoding the gait features to make a
reliable prediction of action intention is the core issue in the brain
computer interface (BCI) based hybrid rehabilitation and intelligent
walking aid robot system. In order to realize the classification and
recognition of the most basic gait processes such as standing, sitting
and quiet, this paper proposes a feature representation method based on
the signal complexity and entropy of signal in each brain region.
Through the statistical analysis of the parameters between different
conditions, these characteristics which sensitive to different actions
are determined as a feature vector, and the classification and
recognition of these actions are completed by combing support vector
machine, linear discriminant analysis and logistic regression.
Experimental result shows that the proposed method can better realize
the recognition of the above-mentioned action intention. The recognition
accuracy of standing, sitting and quiet of 13 subjects is higher than
81%, and the highest one can reach 87%. The result has significant
value for understanding human’s cognitive characteristics in the process
of lower limb movement and carrying out the study of BCI based strategy
and system for lower limb rehabilitation.
Introduction: It has lots of advantages using robot than
traditional artificial method in the rehabilitation training, which can
increase the motivation of patients and the opportunity of autonomous
training, so as to improve the quality and effect of the rehabilitation.
Exoskeleton and intelligent walking aid robot are widely used in the
gait rehabilitation and have achieved good results [1,2]. With the
development of brain computer interface (BCI) technology, researchers
began to pay attention to the BCI based intelligent walking robot and
rehabilitation training technology. It can improve the rehabilitation
strategy by detecting brain’s motion intention more quickly, which is
the development trend of future neurological rehabilitation [3,4].
It is important to investigate the
relationship between brain cognitive activity and motor process in the
development of BCI based active rehabilitation technology.
Electroencephalograph (EEG) is widely used in the detection of motor
intention because of its simplicity, portability and high time
resolution [1,5]. Studies also shown that EEG signal contains
abundant gait and motion information [6], while the decoding
research on lower limb motion intention such as walking and gait has
just started. One of the most basic movements in the gait process is
stand up (standing) and sit down (sitting). Zhong et al investigated the
event related potentials during the attemped standing up task, they
found significant midcentral-focused mu ERD with beta ERS during
imaginary standing up task [7]. Bulea et al. [6] studied the
corresponding EEG features of 10 subjects during the transition between
sitting and standing by decoding the low-frequency band signals, and
combined with Gaussian mixture model (GMM) to realize the recognition of
the two conditions. In the subsequent work, Bulea and Contreras-Vidal et
al. [4] analyzed the feasibility of delta frequency in motor
intention decoding. These signals in standing, sitting and quiet
condition were analyzed by designing two models under self-trigger and
external cue trigger, and the GMM classifier was also used to obtain a
good result. In addition, other decoding studies on motor intention
mainly focus on two types of signals, one is the event-related
synchronization/desynchronization (ERS\ERD) potentials
and the other is the movement related brain potentials (MRPs) [1,4].
Above discussed studies have deepened the understanding of brain
cognitive mechanism corresponding to motor intention, and realized the
effective detection and recognition of the movement. However, these
studies mainly focus on the slow potentials from few electrode channels
in sensorimotor regions, which lack the characteristic information from
spatial domain which considering the interaction between different brain
regions from the whole brain.
It is well known that gait is a complex cognitive and motor control
process, and lower limb movements also involve the coordination and
cooperation of all brain regions [4]. However, before a standing and
sitting action is completed, the brain must show certain characteristic
information and the motion intention can be finally determined by
decoding such information. In addition to the above-mentioned
representation of cortical slow potentials, it is expected to reveal new
features of motor intention decoding through the analysis of dynamic
change process of brain interdependence [8,9]. Lau et al [10]
investigated the characteristics of functional brain network during
standing and walking, and they found that compared with standing
condition, the functional connection of sensorimotor areas would be
weakened during walking. They think it is because it needs more
cognitive attention during walking. Li et al [8] investigated the
features of functional connectivity during rehabilitation with the help
of exoskeleton, and indicating that the graph theory based brain network
analysis has a certain role in the research of gait rehabilitation.
Handiru et al [9] studied the balance of brain trauma patients
during walking by building the functional brain networks and they found
the significant network features for patient walking. However, it is
obviously necessary to carry out further analysis from various
perspective for action intention detection. To this end, this study
designed a motion experiment for sitting and standing actions. EEG
signals were collected synchronously and the brain were divided into
eight regions. The complexity and entropy characteristics of the EEG
signals for eight regions during the whole action onset were fully
analyzed. These features which sensitive to different actions are
screened by a statistical analysis. Finally, the recognition of
standing, sitting and quiet condition is realized by combing several
machine learning classifiers, as shown in Fig.1 is the block diagram of
this study.