8th International IEEE EMBS Conference on Neural Engineering Shanghai, China, May 25 - 28 , 2017
Common Spatial Pattern with Polarity Check for Reducing Delay Latency in Detection of MRCP based BCI System Lin Yao, Mei Lin Chen, Xinjun Sheng, Natalie Mrachacz-Kersting, Xiangyang Zhu, Dario Farina, Ning Jiang*
Abstract:
This work proposes a Common Spatial Pattern with Polarity Check (CSPPC) to facilitate Movement Related Cortical Potential (MRCP) detection. The algorithm was compared with the Locality Preserving Projection (LPP) algorithm in the context of detecting foot dorsiflexion within a group of thirteen subjects. It has been shown that CSPPC achieved a significantly reduced delay latency compared to LPP (-25.9±190.7 ms vs. 204.6±123.7 ms), which had a similar detection accuracy (true positive rate: 73.6±23.3% vs. 72.2±16.3%). This proposed algorithm will enhance the induction of neuroplasticity by significantly reducing the delay between movement detection and the corresponding afferent input. Keyword: MRCP, BCI, Common Spatial Pattern with Polarity Check (CSPPC), LPP
I. INTRODUCTION Brain-computer Interface (BCI) provides a non-muscular channel for communication and control [1], and it enables communication for amyotrophic lateral sclerosis (ALS) patients who are in the completely locked-in state [2][3]. These patients lack muscular control, including ocular movements, while their consciousness is fully preserved. A recent successful BCI application for a locked-in patient with ALS marks a promising milestone in BCI development [3]. In recent year, in addition to communication and control, BCI has gradually attracted an immense amount of interest for stroke rehabilitation [4]–[6], through actively closing the sensory-motor control loop between the motor command and sensory afferent input [7], [8]. Conventionally, sensory-motor rhythm (SMR) is widely utilized for motor intention decoding. Through motor imagery of one’s own limb, the band power decreases or increases in SMR frequency (8-30 Hz). This can be reliably detected [9] and is termed event-related desynchronization or synchronization [10], [11]. SMR-based systems can achieve reasonably high detection accuracy, but its latency, the delay between the motor intention and the corresponding detection, is in the order of seconds [12]. For instance, post-movement beta rebound was investigated for a Lin Yao, Mei Lin Chen, Ning Jiang are with the Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada (
[email protected]). Natalie Mrachacz-Kersting is with Center for Sensory-Motor Interaction, the Faculty of Medicine, Aalborg University, Aalborg, Denmark. Xinjun Sheng, Xiangyang Zhu is with State Key Lab of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China. Dario Farina is with Department of Bioengineering, Imperial College London, London, UK.
978-1-5090-4603-4/17/$31.00 ©2017 IEEE
brain switch, but its delay latency was several seconds, which make it to be impractical for the rapid induction of neuroplasticity [13]. In contrast, a brain switch based on Movement Related Cortical Potential (MRCP) has shown to be able to significantly reduce the delay to about 300 ms [14], [15], thus can potentially increase efficiency in rehabilitation and reduce the rehabilitation time required – conventional rehabilitation requires several months to exhibit any effect [16], [17]. According to the Hebbian facilitation principle, neural structures that fire together would wire together [18], the delay latency between motor intention and the artificially generated appropriate afferents is critical for the success of BCI based neurorehabilitation systems [8], [19]. Hence, reducing the delay latency is a promising avenue for enhanceing rehabilitation. In this study, we will propose and test a novel algorithm for further reducing the latency delay. II. METHODS A. Subjects and EEG/EMG recordings Thirteen BCI naïve subjects were recruited (age: 28.0 ± 5.2 years). The experiments were approved by the local ethical committee and the subjects signed an informed consent form before participation. An active EEG electrode system (ActiveCAP, Brainproducts) and the g.USBamp amplifier (gTec, GmbH) at the sampling rate of 1200 Hz was used. Nine electrodes were placed on Cz, Fz, FC1, FC2, C3, C4, CP1, CP2, and Pz, while the ground electrode and reference electrode were placed on AFz and the left earlobe respectively, according to the standard international 10–20 system. Electrodes were prepared such that their impedances were below the recommended value by the manufacturer of actiCAP system before data acquisition. One channel of surface electromyography (EMG) was also recorded with the g.USBamp amplifier. EMG was acquired in monopolar montage from the tibialis anterior (TA) muscle with disposable electrodes (Neuroline 720, Ambu). The electrode was placed on the mid-belly of the right TA muscle, while the reference and ground electrodes were placed on the bony surface of the right knee and right ankle, respectively. The 9 EEG channels were connected to the first 9 inputs of the g.USBamp amplifier, while the EMG was connected to the last input. The ground and reference settings were done through software configuration, such that the EEG channels
544
Detection/ Arbitrary unit
CSPPC Detection TPR: 90.0% FP: 0.6 n/min Latency: 118±155 ms
Detection/ Arbitrary unit
Figure 1. Illustration of MRCP signals with respect to ballistic foot dorsiflexion task. X represents the signal portion, Y represents the background noise, Z represents the rebound.
had a common ground and reference, while the EMG channel had separate ones. B. Experiment Paradigm Subjects sat still with their forearms and hands resting on the armrest, while minimizing eye blinks and facial or arm movements. They were asked to perform a total of two runs of ballistic dorsiflexion of their right ankle in a self-paced manner, with approximately 30 trials in each run, lasting around 6 minutes. Subjects took a rest between the two runs to avoid muscle fatigue during the experiment.
LPP Time/ Detection Seconds TPR: 73.3% (1) FP: 1.3 n/min Latency: 298±290 ms
Time/ Seconds
Figure 2. Simulated on-line Detection. The upper is CSPPC Detection; the bellow LPP Detection. Vertical black line indicates the onset of the movement detected from EMG signals; Red star indicates the detection of the corresponding algorithm; Blue star indicates that the corresponding detection is a true detection.
is the number of sample points, and k is the trial index. The normalized spatial covariance of the EEG can be obtained from (2)
C. Locality Preserving Projection algorithm Locality Preserving Projection (LPP), as a manifold learning dimensional reduction algorithm, preserves the local intrinsic structure of the data in the original high dimension. LPP has been successfully introduced to MRCP BCI [14], and extensively compared with the matched filter approach. In brief, the temporal projection matrix can be solved from the following generalized decomposition problem: (1) where be a symmetric matrix with each representing the distance between vertices and , is a diagonal matrix whose entries are . The temporal filtering matrix is selected as 60% of the original dimension. Linear Discriminant Analysis (LDA) is used as the classifier. Before LPP temporal filtering, the signals were filtered between 0.5 to 3 Hz using 2nd order butter-worth filter.
where
D. Common Spatial Pattern with Polarity Check Common Spatial Pattern (CSP) is a method that has been successfully applied in MI-based BCI literature [20], and has been widely utilized for a wider range of SMR-based BCI systems. Mathematically, it is achieved by simultaneous diagonalization of the covariance matrices for the two classes. The raw EEG signal is represented as with dimensions , where is the number of recording electrodes, and
The variance operation (square summation) of the signals makes no difference when one signal is a negative deflection while the other is a positive rebound, as shown in Fig. 1; X represents the MRCP signals in deflection phase while Z represents the rebound phase. When applying CSP algorithm, the discrimination between X and Y in Fig. 1 will be enhanced, however, the extracted feature will likely falsely classify the rebound signal Z also as the MRCP. Therefore, we introduced the polarity check to overcome this limitation of
matrix
where
denotes the transpose of the matrix and is the sum of the diagonal elements of the . Let (3) and
are the two index sets of the separate classes.
In the offline analysis of the current study, the signals were filtered between 0.5 to 3 Hz using 2nd order butter-worth filter before CSP spatial filtering. The projection matrix is obtained from the augmented generalized decomposition problem, . The rows of are spatial filters; the columns of are spatial patterns. The log variance of the first two rows and last two rows of (corresponding to two largest eigenvalues and two smallest eigenvalues) were chosen as feature vectors, and LDA was selected as the classifier.
545
CSP in MRCP BCI detection. In the classifier training phase, after CSP spatial filtering, the mean value of the MRCP signal of each trial will be calculated and a threshold will be determined from all trials in the training phase, with the following equation: (4) where is the temporal average of the trials, and equals the standard deviation of , and is manually selected as 0 in this current study. During the subsequent classification, when LDA detects the current portion as signal, the corresponding mean value of the spatial filter signals will be calculated and compared with the aforementioned threshold. If this mean value is above the threshold, then it will be classified as noise although it was classified as signal by LDA. F. Evaluation in Simulated Online Experiment The Teager–Kaiser energy operator was used to detect movement onset from the EMG, which has been shown to be more accurate than using the amplitude of the surface EMG. The corresponding onset time point was used for the following evaluation analysis: the true positive rate (TPR) was the ratio between the number of true detections and the number of total attempts, identified from the EMG when performing real movement tasks. The false positive (FP) was the number of false detections per minute. In addition, the detection latency (DL) was calculated by taking the latency between the time of the detection and the EMG onset of corresponding movement for the executed tasks. In the simulated online testing scenario, the signal was continuously classified with step size of 100 ms; the window length of CSP is 400 ms, while the window length of LPP is 2000 ms. When two consecutive windows were classified as MRCP signals, a detection would be marked. There was a refractory time period of 2 seconds, in which no detection is possible. III. RESULTS In this study LPP and CSPPC were only compared in the
scenario of simulated on-line with real-movement task. Fig. 2 illustrates the detection from the two algorithms when subjects performed real-movement. Table 1 outlines the simulated on-line results of all subjects. A paired-T test was applied to compare the difference of the two algorithm in TPR, FP and DL. There was no significant difference between CSP and LPP in TPR with p = 0.863. No significant difference was found with respect to FP with p = 0.303. The delay latency was found to be significantly reduced from 204 ms to -26 ms with p = 0.002. IV. DISCUSSION By applying the CSP spatial filtering and extracting the low frequency band power feature, the CSPPC algorithm has shown promising results in significantly reducing the delay latency between the task and detection (p = 0.002). This was compared with the state-of-the-art LPP algorithm, and the TPR and FP showed no significant difference. The proposed algorithm was only tested on real-movements of foot dorsiflexion task in a simulated on-line scenario. Future on-line experiments need to be conducted, and motor imagery or attempted movement should also be investigated. V. CONCLUSION In this work, we proposed a Common Spatial Pattern with Polarity Check (CSPPC) algorithm to further reduce the delay latency in MRCP based BCI system. In real-movement and simulated on-line scenarios, the true positive rate and false positive rate were comparable to LPP algorithm, while the detection latency was significantly reduced. ACKNOWLEDGMENT We thank all volunteers for their participation. This work is supported by the University Starter Grant of the University of Waterloo (No. 203859), the National Natural Science Foundation of China (Grant No. 51620105002, 51375296, 51421092), the Research Project of State Key Laboratory of Mechanical System and Vibration MSV201607.
Table 1. Performance of Each Subjects with Respect to TPR, FPR and DL
TPR (%) FPR (n/min) DL (ms)
subject
1
2
3
4
5
6
7
8
9
10
11
12
13
mean
CSP
90.0
93.3
55.6
91.3
65.4
85.2
93.5
54.3
13.6
87.5
60.6
90.0
75.8
73.5±23.3
LPP
73.3
93.3
61.1
69.6
69.2
48.1
74.2
60.0
90.9
100.0
54.5
86.7
57.6
72.2±16.3
CSP
0.7
1.5
9.3
4.5
1.7
5.4
5.5
0.6
2.7
0.9
2.4
2.1
3.8
3.2±2.5
LPP
1.3
2.7
5.3
4.3
2.0
4.4
5.7
2.0
4.8
2.8
6.2
3.0
3.6
3.7±1.6
CSP
118.7
-34.1
51.3
88.7
100.7
-124.9
29.4
53.6
-565.0
177.2
-158.7
38.0
-111.9
-25.9±190.7
LPP
298.3
188.0
-12.7
202.4
452.7
183.1
42.2
238.1
211.3
202.7
157.4
374.0
122.8
204.6±123.7
546
17849–17854, 2004.
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