Preliminary Study of EEG-based Brain Computer Interface Systems for Assisted Mobility Applications

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1 Preliminary Study of EEG-based Brain Computer Interface Systems for Assisted Mobility Applications L. Gao, J. Qiu, H. Cheng, J. Lu School of Mechanical Engineering, University of Electronic Science and Technology of China, China Abstract: As a consequence of an increasingly elderly population, accommodating age-related impairments has become a serious concern in many countries. To assist people with disabilities, the use of electroencephalography (EEG)-based brain computer interface (BCI) systems has been explored and has proven to be both clinically usable and feasible. In this study, four mental tasks that are directly related to mobility commands were implemented into the simulated design of a BCI-controlled exoskeleton. These mental tasks were used in a BCI training procedure. EEG signals were recorded using a modified emotive headset and were analysed in the time frequency domain. CSP patterns were used to evaluate the data acquired during the completion of the mental tasks. A classifier that is based on the Support Vector Machines (SVM) method was used. The average classification error for the ten participants was and the lowest error rate was In the two two contrast groups, two different patterns from one group showed a large area of overlap, indicating poor performance of the CSP method for this BCI training procedure. This may have been caused by the motor imagery area overlap of the four mental tasks. Practitioner Summary: The high average classification error implies that while the chosen complex mental tasks may be appropriately chosen for BCI training procedure, the features of comparison should be chosen more carefully. More research into the features of these mental tasks is required as they have a large influence on their classification results. Keywords: Mental tasks, EEG, BCI, CSP, Exoskeleton 1. Introduction The global proportion of senior citizens, i.e., 60 years of age or more, will reach 21.1% by 2050 (United Nations 2013). In China, the increase in the elderly population will be increasingly serious; the proportion of senior citizens was found to be 13.3% in 2010 and is expected to reach 34% by According to the Sixth National Population Census conducted in 2010, China had more than 24 million disabled people (China 2010). Thus, accommodating age-related impairments has become a serious concern in many countries. The ability of the senior citizens and disabled people to independently function within the community is a critically important public health issue. To assist those with walking disabilities, a few lower-limb exoskeletons have been developed, including EKSO, Rewalk, HAL and Rex (Contreras & Grossman 2013). The usual means of cognitive human exoskeleton interaction are to interface with manual buttons or with a person s centre of gravity. Compared to control methods based on the use of position sensors, electroencephalography (EEG)-based brain computer interfacing (BCI) is more intuitively the human brain controls all motor functions. Using EEG-based BCI, the gait command is registered and activated by the central nervous system (Vaughan, Davis, and O Connor 1999). Hence, human walking intention can be controlled by

2 brain activities, activities that can be described through use of an electroencephalogram. EEG-based signals can be extracted and modeled for human walking intention and provide commands to exoskeletons. A few studies have recently been conducted to improve assisted human walking. Hortal et al. (2013) researched the online classification of the BCI system, with the aim of improving the independence of the movement disabilities. There were 12 different mental tasks discussed in their research that include the movement of left and right hand, repetitive movement of the left and right legs, imagination of a cube rotation, mental regressive counting, performance of mathematical operations and alphabet recitation. Participants rapidly mastered an asynchronous EEG-based BCI to control a wheelchair in a study conducted by Galán (2008). In his study, the participants executed three different mental tasks: imagination of left hand movement, rest and word association. Gao et al. (2003) designed a BCI-based environment controller for motion-disabled persons where blinking buttons on the computer monitor, with frequencies varying from 6 to 15 Hz, were used as stimulation. In the famous Graz BCI, the most commonly used mental tasks were left- and right-hand movement imagery (Obermaier, Müller, and Pfurtscheller 2001), which were sometimes combined with the imagination of feet movement, mental subtraction and word association (Faller et al. 2012). There were eight mental tasks performed in the study of a non-invasive BCI aimed at enhancing the control of games and robots (Scherer et al. 2011): mental rotation, word association, auditory imagery, mental subtraction, spatial navigation, imagery of faces and motor imagery of the right hand. Three mental tasks baseline, letter and mathematical were used as classifications in the paper written by Hosni in 2007 to compare different feature-extraction techniques. In other words, while there are some commonalities between previous EEG-based BCI studies, there are no definitive baseline tasks used by all. However, the EEG-based BCI systems described above were almost all based on relatively limited natural motor imagery or visual stimulation; for example, a wheelchair s movement control using left- and right-hand movement imagery. Mental tasks such as the imagery of cube rotations or mathematical calculations were not directly related to control commands such as moving forward or standing up. We have focused our efforts on developing an UESTC-EXO exoskeleton to help people with motor impairments improve their quality of living. To develop a more natural EEG-based BCI, four mental tasks have been directly related to the control commands for an exoskeleton: the imagination of sitting down, standing up and turning left and right. On the basis of these tasks, a BCI system was trained offline. These training results will help to increase our understanding of the influence of mental tasks on the BCI system. 2. Methods 2.1 Experimental Design To build a simulation of exoskeleton control, four mental tasks that simulate the daily sitting and standing activities of a person with complete mobility were selected for a BCI training procedure. These tasks are listed below and were operated on a free psychology program (Mueller 2014) named PEBL (psychology experiment building language).

3 Turn left: Participants, seated in a comfortable chair, imagined themselves standing in front of the screen and turning to the left when prompted by a left-pointing arrow displayed on the screen. Turn right: Participants, seated in a comfortable chair, imagined themselves standing in front of the screen and turning to the right when prompted by a right-pointing arrow displayed on the screen. Sit down: Participants, seated in a comfortable chair, imagined themselves standing in front of the screen and turning to the left once the screen displayed an upwards-pointing arrow. Stand up: Participants, seated in a comfortable chair, imagined themselves sitting in the chair in front of the screen and turning to the left once the screen displayed a downwards-pointing arrow. The experimental setup included a chair placed 160 cm in front of a 21-inch LCD display screen on a 70-cm-high standard work table. The chair s height was adjustable and included a backrest. EEG signals were acquired by a modified emotive headset combined with an EEG-recording cap that comprises 14 attached electronic channels. Due to the cap s small ( mm) and lightweight (48 g) design, the participants could interact with it in a comfortable manner. Once the cap was fitted, good conductivity was confirmed using the E-motive software. All data was translated from the hardware to the computer and subsequently stored by the software (Debener et al. 2012). 2.2 Participants Ten participants, which were selected to form a homogeneous group, were recruited for the study. Their ages ranged from 22 to 25 years (mean = 23 years; SD = 0.7). All of the participants were right-handed, were healthy with no history of severe illness and had no previous experience wearing a BCI device. 2.3 Experimental Procedure Participants were trained in groups of 10 over the course of 10 sessions. In each session, the four mental tasks were allotted in a random order. Between each group session, there was an interval for rest that lasted 2 5 min. Each mental task took 3 10 s to complete. 2.4 Data acquisition The use of excessive electrodes would have negatively impacted the performance of the participants and would not have been feasible for daily patient use. Therefore, 14 electrodes were selected for data acquisition. These 14 electrodes were made of sintered Ag/AgCl and positioned using the International system. The chosen channel sites were F7, F3, Fz, F4, F8, C3, Cz, C4, P7, P3, Pz, P4, P8 and Oz. In order to be fixed onto the scalp of the participants, these electrodes were inserted into a modified EEG-recording cap whose elasticity allowed to accommodate various head sizes. The positions for all 14 electrodes are shown in Figure 1.

4 Figure 1. The positions the 14 electrodes on the EEG-recording cap. 2.5 Data Analysis After the EEG signal was recorded, automatic noise elimination was utilised. The processed signal was then analysed in a time frequency domain. The event-related potential (ERP), the EEG signal spectra and the event-related spectral perturbation (ERSP) were calculated using EEGLAB, which is an open-source MATLAB toolbox. The common spatial pattern (CSP) method was used as the spatial filter for the EEG signal and the two-class classification was always applied. Through this method, the EEG signals were converted into different CSP patterns. These patterns retained the EEG signal information and were sorted for classification. The best CSP groups were the first and the last patterns, which contained the most effective information for the two classes. To address the CSP method s limitations, its patterns were determined through six two two contrast analyses. A classifier that is based on the Support Vector Machines (SVM) method was used. The results were statistically analysed by SPSS 21 software with the significance level set to The data analysis procedure for each subject is summarised in Figure 2. Figure 2. Data analysis procedure for each participant. 3. Results 3.1 Time frequency analysis The ERP is the measured brain response that is a direct result of a specific motor event. Figure 3 shows the ERP plots from site Cz for Subject 2 during completion of the four mental tasks. In this figure, it can be found that the ERPs differ between the mental tasks. Figure 4 shows the full-frequency range spectra from site Cz for Subject 2 during the completion of the four mental tasks and the spectra are almost identical for each tasks. The event-related spectrum perturbation (ERSP) measures the average time course of relative changes in a spontaneous EEG amplitude spectrum. Figure 5 shows the differing ERSP spectra from site Cz for Subject 2 during the completion of the four

5 mental tasks. However, in the time interval from 1500 to 200 ms, the ERSP of each mental task exhibit similar patterns in the 3 5 Hz frequency band. Figure 3. The ERP spectra at site Cz for Subject 2 during completion of the four mental tasks. Figure 4. The EEG signal spectra from site Cz for Subject 2 during completion of the four mental tasks. Figure 5. The ERSP from site Cz for Subject 2 during completion of the four mental tasks. Red and blue colors indicate regions of high and low activity, respectively. 3.2 CSP patterns For ERD/ERS-based BCI systems, the CSP method is usually useful as a feature-extraction artifice for the classification of mental tasks. The CSP method decreases the dimension of the

6 classifier input while preserving the most effective information from the EEG signals for the discrimination of different tasks. However, there are limitations to the CSP method as it can only detect the differences between two tasks. Therefore, herein, the CSP patterns were derived using six two two contrast analyses. The best three groups of CSP patterns were calculated for each contrast and the best groups for each contrast pair are shown in Figure 6. Figure 6. The best CSP groups for each contrast pair. In this figure, D indicates sitting down, U indicates standing up, L indicates turning left and R indicates turning right. Red and blue colors display regions of high and low activity, respectively. 3.3 Classification error Since the classifier is based on the CSP method, the results of our classification were analysed in two two contrast pairs. The training results are shown in Table 1. In this table, D/U represents the contrast pair sit down-stand up, D/L represents sit down-turn, D/R represents sit down-turn right, U/L represents stand up- turn left, U/R represents stand up- turn right and L/R represents turn left- turn right. Table 1. The classification error rate of six contrast pairs for each subject. ERROR S01 S02 S03 S04 S05 S06 S07 S08 S09 S10 D/U D/L D/R U/L U/R L/R The average classification error for the 10 participants is Since the error rate was not as low as expected, the classification processing had to be carefully analysed. First, ANOVA was used to consider the effects of the participants and the contrast pairs toward the results. ANOVA revealed that the classification error rate was significantly influenced by the contrast groups (F = 6.04 > F (5, 45)), with a significance level p lower than Discussion

7 BCI classification training based on the four chosen mental tasks was obtained with a relatively high error rate. The lowest error rate was Since our BCI classification did not achieve the expected results, it was necessary to examine the results obtained herein. 4.1 The interpretation of the time frequency features From the event-related potentials of the different mental tasks, differences were found between each task s patterns, although there was little change in the ERP s absolute value. Furthermore, these values did not change a significantly over time. Although the ERP patterns seem to be distinct, the differences are not particularly easy to resolve, which may be one explanation for the high error rate. The EEG signal spectra were virtually identical for the different mental tasks. In human brain activities, the rhythm amplitude decreases when the rhythm frequency increases. This means that the spectra were mainly reflected by the slower waves. In this BCI training procedure, the four performed tasks were complex and may have activated the frontal and motor areas of the cortical cortex. The cortical cortex's signals are primarily alpha rhythms (8 13 Hz) and beta rhythms (13 Hz), which have lower amplitudes than the detected slow waves. Once an EEG signal spectrum was calculated, the BCI system classifier used the slower waves as the feature for task discrimination. Although not the principle objective of this study, the feature classification may be able to be interfered from the EEG signal spectra. The event-related spectrum perturbation from the EEGs for the different mental tasks differed from one another. The ERSP of the EEG signals for the different tasks were distinct. When comparing the ERSPs from each task, little variation was observed at high frequency bands. At low frequency, there was more activity and the ERSP patterns changed. The changing patterns of the active frequency bands for the different mental tasks somewhat resembled one another, while the high-frequency ERSP bands seemed inactive. Thus the effects of the features for the classifier may be attenuated, leading to poor classification results. 4.2 Interpretation of CSP Patterns The best CSP groups for each contrast pair were shown in Figure 6. Even for the CSP pattern groups that provided the best classification distinction, the two patterns still showed a large area of overlap. The implemented CSP method used as a spatial filter for this BCI training procedure did not perform well, possibly due to the complex features of the selected mental tasks. Though these four tasks simulated the daily sitting and standing activities of a mobile person, which at first glance would seem to be more natural to perform than other mental tasks, each of the four tasks was complex and, thus, was difficult to implement for the participants. Even if the mental task was well performed, a large active area of the brain was engaged. However, the brain activity during the completion of each mental task was closely related to the brain area influenced by the equivalent motor movement. Therefore, a large area of overlap with the motor area is unavoidable. 5. Conclusion The classification error rate was significantly influenced by the contrast groups. The event-related potential (ERP), the EEG spectra and the event-related spectrum perturbation (ERSP) were calculated. The average classification error for 10 participants was and the lowest error rate for the results was The training results for the participants did not improve over time. Furthermore, the feature selection of the mental tasks was found to not be suitable for a BCI procedure using CSP methods. Further studies should utilise mental tasks focused in alternate areas of brain signal activity.

8 This study suggests that complex mental tasks are an appropriate choice for a BCI training procedure; however, the features of the mental tasks should be carefully selected. These results clearly indicate that more research on mental task features and their classification method is required prior to the improvement of cognitive human-exoskeleton devices. Acknowledgements This study was supported by the Natural Science Foundation of China under Grant number and the China Postdoctoral Science Foundation under Grant number 2014M Reference China Major data bulletin of the sixth national census in China. Contreras-Vidal, J. L., and R. G. Grossman NeuroRex: A clinical neural interface roadmap for EEG-based brain machine interfaces to a lower body robotic exoskeleton. In Engineering in Medicine and Biology Society (EMBC), 35th Annual International Conference of the IEEE, Debener, S., F. Minow, R. Emkes, K. Gandras, and M. de Vos How about taking a low-cost, small, and wireless EEG for a walk? Psychophysiology 49 (11): Faller, J., C. Vidaurre, E. V. C. Friedrich, U. Costa, E. Opisso, J. Medina et al Automatic adaptation to oscillatory EEG activity in spinal cord injury and stroke patients. In TOBI workshop 1: 7-8. Galán, F., M. Nuttin, E. Lew, P. W. Ferrez, G. Vanacker, J. Philips, and J. D. R. Millán A brain-actuated wheelchair: asynchronous and non-invasive brain computer interfaces for continuous control of robots. Clinical Neurophysiology 119(9), Hortal, E., A. Ubeda, E. Iánez, D. Planelles, and J. M. Azorín Online classification of two mental tasks using a SVM-based BCI system. In 6th International IEEE/EMBS Conference on Neural Engineering (NER), Hosni, S. M., M. E. Gadallah, S. F. Bahgat, and, M. S. AbdelWahab Classification of EEG signals using different feature extraction techniques for mental-task BCI. In International Conference on Computer Engineering & Systems, Mueller, S. T PEBL: The Psychology experiment building language (Version 0.14) [Computer experiment programming language]. Obermaier, B., Müller, G., & Pfurtscheller, G Virtual Keyboard controlled by spontaneous EEG activity. In Artificial Neural Networks ICANN, , Berlin Heidelberg: Springer. Scherer, R., E. C. Friedrich, B. Allison, M. Pröll, M. Chung, W Cheung et al Non-invasive brain-computer interfaces: enhanced gaming and robotic control. In Advances in Computational Intelligence, Berlin Heidelberg: Springer. United Nations World Population Ageing New York. Vaughan, C. L., B. L. Davis, and J. C. O Connor Dynamics of human gait (Second Edition). Western Cape, South Africa: Kiboho Publishers. Gao, X., Xu, D., Cheng, M., Gao, S A BCI-based environmental controller for the motion-disabled. IEEE Transaction on Neural Systems and Rehabilitation Engineering 11,