Participants

N = 17 healthy participants were recruited for this study. We excluded one participant due to incompliance with the experimental protocol and one participant stopped before the end of the experiment. The final sample thus comprised N = 15 participants (12 female, mean age: M = 21.8 years, SD = 2.9, range 18–27 years). All had normal or corrected-to-normal vision and none reported any neurological disorders. All participants were naïve with regard to tactually evoked ERP-BCIs. We conducted the experiment in accordance with standard ethical guidelines as defined by the Declaration of Helsinki (World Medical Association) and the European Council’s Convention for the Protection of Human Rights and Dignity of the Human Being with regard to the Application of Biology and Medicine (Convention on Human Rights and Biomedicine). All participants gave written informed consent prior to the study. The study was approved by the ethics committee of the Institute of Psychology at University of Würzburg, Germany.

Equipment and data acquisition

Eight tactile stimulators, i.e. vibrate transducers (C2 tactors; Engineering Acoustic Inc., Casselberry, USA), were grouped into pairs of two and attached to a participant’s left thigh (top, toward knee), right thigh (top, toward knee), abdomen (above navel) and lower neck (at the height of C4 to C8) using Velcro® belts. Prior to the experiment participants had the opportunity to stimulate all tactors individually, to ensure that they adequately perceived all stimulations. During the experiment, each pair of tactile stimulators constituted one target, i.e. two tactors at close position were stimulated simultaneously. We found that grouping two tactors into one target facilitated participants' recognition of stimuli in a pilot study. Stimulus duration was set to 220 ms and inter-stimulus interval to 400 ms. Stimulation frequency was 250 Hz.

EEG was acquired from 16 passive Ag/AgCl electrodes at positions Fz, FC1, FC2, C3, Cz, C4, CP1, CP2, P7, P3, Pz, P4, P8, O1, Oz and O2 ([5]) with ground and reference being applied to right and left mastoid respectively. Impedance was kept below 5 kΩ. Signals were amplified using a g.USBamp (g.tec Engineering GmbH, Graz, Austria) and recorded at a sampling rate of 512 Hz. Band pass filtering between 0.1 and 60 Hz and notch filtering between 48 and 52 Hz were applied online.

Software implementations

Feedback paradigms

Participants were guided through the calibration and copy task runs (see section “Study design”) such that the current target was displayed on a screen, i.e. target positions on the body were presented in a schematic side- and top view. Figure 1A provides a screenshot of the presented display during the calibration phase. The same display was also presented during the copy task runs except that feedback on the outcome of classification was provided in real time. We implemented the paradigms in Python 2.5 (using Pygame 1.9 and PyOpenGL 3.0) and connected them to BCI2000 via user datagram protocol (UDP). Feedback paradigm and BCI2000 were executed on separate computers.

Offline and online classification: dynamic stopping and static stopping

We refer to classification based on data acquired during a calibration run as offline classification, whereas online classification is classification that is performed during ongoing data collection and results in immediate feedback to the user.

During online runs, data were streamed into MATLAB 2010b (The Mathworks Inc., Massachusetts, USA) using Fieldtrip ([65]; http://fieldtrip.fcdonders.nl). Online classification (stepwise linear discriminant analysis, SWLDA, 800 ms post-stimulus; as e.g. used in [39, 66, 67]) was then performed in MATLAB and results communicated to the feedback applications by means of UDP.

We implemented a dynamic stopping based on a combination and modification of two recently published dynamic stopping methods ([56, 57], see introduction). Figure 2 illustrates the decision tree. The tree comprised three basic rules as follows. (1) A minimum number of three sequences were collected for classification. (2) If no decision could be made after gathering a predefined maximum number of sequences (NoS), the most likely target was classified from all gathered sequences of the trial. The maximum number of trials was adjusted for each participant separately based on results from calibration (minimum NoS to reach offline performance estimation of stable 100% plus two sequences; described in detail in [68]). (3) A dynamic stop could be performed if the most likely target was the same three sequences in a row (modified from [57]) or if a t-test with unequal variance performed on so far gathered samples was significant at an alpha level below 10% (modified from [56]). The alpha level was chosen after pilot testing.

We compared dynamic stopping to the commonly used static stopping, i.e. each trial comprised a fixed number of sequences that were all used for classification. The number of sequences was equal to the maximum number of sequences used in the dynamic stopping run.

Study design

Before the experiment, participants were instructed and tactors were placed (see section “Equipment and data acquisition”). Participants had the possibility to adjust tactor positions by a few centimeters until they perceived all stimulations equally well. To familiarize the participants with the floor map and with the control principle of the virtual wheelchair, they used a keyboard to move the wheelchair through the virtual environment during EEG preparation.

The actual experiment consisted of one calibration run (predefined task; data is used to compute classifier weights), two copy tasks (predefined task; used to evaluate classifier performance online) and finally the main goal of the study, i.e. one task aiming at navigation through the virtual building. Duration of calibration was 10 min. Duration of copy and navigation tasks were participant specific depending on their performance (see section “Results”). One calibration trial comprised 15 stimulation sequences per tactor pair, i.e. each tactor pair vibrated 30 times (one sequence corresponding to four row and four column flashes in the visual matrix; see section “Software implementations - Tactile stimulation”). Calibration was performed with eight trials (each tactor pair was twice the target). If offline analysis revealed a performance below 100% after these eight trials (when including all sequences into classification), we repeated calibration once. After calibration, participants performed two copy task runs. One copy task run included static number of sequences, i.e. each trial comprised a maximum number of sequences before classification. A second copy task run introduced the above-described dynamic stopping method. This allowed for with-in comparison of performance achieved with and without dynamic stopping. During both copy tasks, immediate feedback on classification outcome was provided to the participants. As for the calibration run, each tactor pair was twice the target, resulting in eight trials per copy task run. Participants then moved on to control of a virtual wheelchair and tried to navigate along the predefined route (see section “Software implementations – Virtual environment”). When reaching one of the four checkpoints, they took a break of approximately one minute before moving on (the BCI was manually switched off during this time by the experimenter). The number of trials during navigation varied dependent on the participants’ performance. In the optimal path (Figure 1B) selection of the “move forward” command was required most frequently. However, as errors had to be corrected, the number of required commands per navigation direction differed between participants.

Offline data processing of ERPs

EEG data were filtered between 0.1 and 30 Hz (FIR equiripple) and divided into segments of 800 ms post-stimulus. Determination between targets and non-targets was quantified by computing R² values. For computing the grand average of R² values we Z-transformed (Fisher’s Z) the square root of the determination values for each participant and electrode, averaged across participants and finally retransformed and squared these grand averages.

Analysis of system performance

In the virtual environment, performance estimation is difficult, as different paths may be feasible for reaching the checkpoints. For example, after an error participants may either steer back by one step or take a different path to approach the next checkpoint. Thus, we asked participants to report during the breaks whether or not the selected targets were the desired targets and performance was computed based on their reports. To control for false reporting, we manually went through each decision and decided if it was goal-oriented. Finally, we aligned these two analyses. Except for two selections, these decisions were similar to the subjects reports (265 selections in total; from the two selections one would slightly increase performance estimate, one would slightly decrease performance estimate). Therefore, we consider adequate to estimate performance based on subjects reports.

The impact of shared control was determined from the number of collisions and the number of times when sensors for slowing down speed were active. Furthermore, we computed the time required for delivering commands from the duration of stimulus and inter-stimulus intervals. Classification time, wheelchair movement duration and duration of the breaks the participants took at each checkpoint were not taken into account. Thus, the reported time is system independent and includes only the mandatory time needed for stimulation.

Furthermore, following the user-centered approach we validated the system based on user reports. Participants rated their confidence with tactile ERP-BCI based wheelchair control with forced choice questionnaires. The questions covered learnability, strain, level of control, speed of the system and participants’ trust in the used BCI technology [10].

Statistical analysis

We checked data of achieved BCI performance for normal distribution using Lilliefors - Kolmogorov Smirnov tests. Due to non-normal distributions, we performed pairwise testing with the Mann–Whitney U test. Bonferroni correction to 5% alpha levels is indicated. Statistical analysis was performed in Matlab 2010b.

Dynamic vs. static stopping

We validated tactile stimulation for ERP elicitation online in two copy tasks. Participants gained overall high accuracy levels in both tasks (see Figure 4A). Average accuracy with static number of sequences was M = 90.8% (SD = 13.7, range 62-100%) and nine of 15 participants performed without errors. The time needed to fulfill the task with static stopping ranged from 4.2 to 8.2 min (M = 6.1, SD = 1.2 min), whereas the time needed to fulfill the task with dynamic stopping ranged from 2.6 to 5.4 min (M = 3.7, SD = 1.0). Performance did not significantly decrease when introducing dynamic stopping (N = 15, Z = 0.70, p = .48; M = 84.2%, SD = 23.4), i.e. most participants maintained the performance level achieved with static number of sequences. However, performance for two participants (participant 6 and 15) severely decreased - for participant 15 even to chance level (25%). Furthermore, we investigated if errors were equally distributed across targets. The total amount of errors did not differ between the targets (left: 10% errors of all left target selections; right: 11.7%; forward: 13.3%; back: 15%; N = 15, H (3) = 0.97, p = .81, Bonferroni adjusted alpha level: α = .0083).

Figure 4B depicts the average number of sequences needed to deliver a command. In line with previous reports, the number of sequences significantly decreased in the dynamic stopping copy task (N = 15, Z = 3.81, p < .001). Consequently participants on average needed M = 27.2 seconds per selection as compared to M = 44.6 seconds in the task with static number of sequences.

Wheelchair navigation

Participant 15 did not perform the navigation task as the performance decreased to chance level when using dynamic stopping in the copy task (section “Results – Dynamic vs. static stopping”). Thus, only N = 14 of 15 participants performed the navigation task through the virtual building. For each participant, Figure 5 illustrates the path along which they steered the virtual wheelchair. Importantly, N = 11 participants reached the targeted desk at checkpoint 4 and four participants made no error. Although the navigation task can be regarded as more complex than a simple copy task, performance did not significantly decrease in the virtual environment (N = 14, Z = 0.33, p = .74). Average accuracy was M = 85.8% (SD = 17.6, range 37.5-100%) with a mean of M = 5.58 sequences. Three participants, however, could not successfully finish the task and performed the experiment only until they communicated to prefer canceling. Two of them at least managed to pass the corridor before quitting whereas participant 6 again had almost no control (due to dynamic stopping, see section “Results – Dynamic vs. static stopping”) and thus canceled the experiment early.

ERP differences in target vs. non-target trials

During stimulus duration, tactile stimulation of non-target positions also evokes an event-related response as participants directly perceive all stimuli on the body and cannot easily ignore them. Yet after around 300 ms, target and non-target signals diverge. Target stimulation elicits a P300, whereas non-target stimuli often entail a negative ERP in the period between 300 and 500 ms post-stimulus. ERP responses differed considerably between participants, yet for all of them discrimination between target and non-target stimuli was possible (see Figure 6). Figure 7 provides a topographical map of the grand-averaged ERPs across all participants based on calibration data.

We further computed the determination coefficients to investigate which features contribute most to classification. As depicted in Figure 8, the centro-parietal electrodes contributed most to discrimination between targets and non-targets. Determination coefficients were highest between 400 and 500 ms, i.e. in the time window of the tactile P300.

Subjective validation with questionnaires

Tactile ERPs for BCI based wheelchair control

We exposed participants to a virtual environment and asked them to navigate a virtual wheelchair by means of a tactually evoked event-related potential based BCI. Our results are promising in that most of the participants reached the final checkpoint and that only few participants needed shared control.

Importantly, N = 14 of 15 participants reached 100% offline classification accuracy and one further participant had an offline accuracy level of 87.5%. In all three online tasks, performance of N = 11 participants remained above 70%. For two further participants performance may have remained high (participant 6) or at least medium (participant 15) if we would not have switched to the dynamic stopping method. Tactile ERP-BCIs may thus offer a valuable alternative to motor imagery based BCIs considering the findings that many SMR-BCI users do not gain sufficiently reliable SMR control [31–35]. Also, SMR-BCIs usually require a longer calibration phase than ERP-BCIs and intensive user training may be necessary to achieve a good level of control, specifically in people with neurodegenerative disease [8]. However, performance varied considerably between participants implying the need for testing larger groups for generalization of results, which is hardly ever the case in studies that use BCI for wheelchair control (e.g., N = 2 in [11, 12, 16, 30, 46]; N = 3 in [69]; N = 5 in [20, 44, 47, 48]; and N = 6 in [22]). Furthermore, often healthy users with prior BCI experience were selected thereby also hampering generalization of results (e.g., [30, 70]). Since all our participants were naïve with regard to tactile ERP-BCIs, we speculate that a studious learning of tactile perception (in particular learning to ignore irrelevant tactile stimulations) may further enhance their performance. Furthermore, rebuilding classifiers based on more data input may increase performance, as the short calibration performed at the beginning of the experiment may not be sufficient.

Consequently, in case more data would further enhance classifier accuracy, generic models could be of high value to shorten calibration time (i.e. building a classifier based on data from a large pool of participants; e.g., [71, 72]). Also, such models may increase performance of those participants who do not achieve accurate control with their individual classifier [73]. However, our results show large inter-individual differences of the ERPs elicited post-stimulus. In line with previous reports (e.g., [54]) the tactually-evoked P300 peaked at central electrodes with an average latency around 400-500 ms. Centro-parietal electrodes contributed most to classification accuracy. Considering the varying ERP responses across participants, recording from more electrode sites could further enhance subject-specific ERP detection and facilitate investigation of generic models.

Our study design built on prior work on tactile ERP elicitation. Brouwer and van Erp [52] found no performance difference with regard to a number of two, four or six tactile stimulators. We thus implemented a system based on four tactors representing direction control units. Thurlings and colleagues [54] investigated how congruent tactor positioning affects task performance. They positioned a monitor vertically or horizontally in front of participants. A control display mapping was realized with tactors positioned either congruent with monitor angle (i.e. horizontal tactor positions around the waist for horizontal monitor placement and vertical tactor positions on the participants’ back in the case of vertical monitor placement) or incongruent (i.e. horizontal tactor positions around the waist and vertical monitor placement). The authors demonstrated that a congruent setup yielded increased P300 amplitudes and thus increased estimated BCI performance. Therefore, in our study we aligned tactor placement with movement directions. With regards to stimulus timing we opted for an on-time of 220 ms and an off-time of 400 ms, i.e. a similar timing than the baseline condition from Brouwer and van Erp [52] in experiments 1 and 2 (188 ms on-time, 367 ms off-time). The authors suggested matching on- and off-times and found this condition to enhance bit-rate while maintaining the performance level. Such adjustment may thus also be feasible for our proposed system. However, due to the increased probability of ERP overlap when reducing off-times, we chose the longer duration.

In contrast to Brouwer and colleagues [52], who chose only the front tactor as target, our calibration and online copy tasks comprised equally often all tactors as target. Our results thus account for perception differences or attention difficulties between different body locations. Some participants may for example perceive the front target (close to the navel) stronger than the back target. In our study participants performed equally well on selection of tactors, i.e. in total participants did not perform significantly more errors on any of the targets than on others. Especially in light of a BCI with manifold selection options (realized placing many tactors on the body), it is inherently important to adjust tactor locations according to users’ reports so that they perceive all targets (approximately) equally well.

In line with previous reports from visual and auditory ERP-BCIs (e.g., [56–60]; for comparison of techniques [62]), dynamic stopping was of high value also for tactile ERP-BCIs. Participants greatly benefited in terms of time needed to deliver commands, thereby increasing speed of the system. Importantly, the reduced number of sequences in the dynamic stopping copy task did not affect performance (no significant difference between static and dynamic stopping copy task performance) except for two participants who displayed a strong performance drop during dynamic stopping. Hence, these participants did not benefit from dynamic stopping. From the offline classification results as well as from task performance in the copy task with static stopping we assume that participant 6 may have successfully performed the navigation task when using a static number of sequences. As participant 15 did not perform a navigation task, we do not know whether the drop in performance was due to bad performance in one run or due to dynamic stopping. In a comparison of dynamic stopping methods, Schreuder and colleagues [61] reported that some methods decrease performance of participants with less discriminative data. Considering the fact that offline classification performance of participant 15 displayed aggravated discriminability compared to other participants’ data, the performance drop may be attributed to dynamic stopping. For all participants, user specific parameter adjustment (as performed by e.g., [56]) could have further increased performance of the dynamic stopping method, especially in the case of those two participants. This may have prevented the algorithm from stopping too early although classification of the target was not sufficient.

Validation of the system based on questionnaires revealed that tactile ERP-BCI based wheelchair control is quickly learnable by naïve participants. Device satisfaction regarding reliability and control was mostly positive. However, evaluation results for demand of control and speed of the system varied and were independent of users’ performances. To better estimate these aspects, longer navigation tasks will be needed. On the one hand, learning to perceive stimuli may positively affect the demands for the user, on the other hand long navigation tasks may further increase demands on attention. Users of such systems in daily life navigation tasks may judge speed of the system more critically.

Limitations and future experimentation

This study explored feasibility of the proposed BCI system in healthy users. We assessed user confidence with forced choice questionnaires to identify remaining issues and how they depend on task performance. However, validation may strongly vary with users’ health and with their actual dependence on the technology. Further research must investigate use of tactile ERP-BCIs by the actual target population. In the process of user-centered BCI development, potential end-users with severe motor impairment should be integrated into the design process at an early stage, so that research can specifically account for their needs and requirements ([6, 10, 63, 74–76]). Furthermore, the effect of proposed improvements may well be larger in patients as compared to healthy participants (as recently found for a modification of visual ERP-BCIs; [7]). In particular, we suggest including patients with SMA type II who we consider a potential target group for use of BCI based wheelchair control. With progression of disease, they usually lose the ability to control a wheelchair with a joystick. Eye-tracking devices would occupy the visual channel needed for observation of their environment and devices based on facial muscles may be too fatiguing. Progression of the disease is usually slower than for example for patients with amyotrophic lateral sclerosis, which renders it more feasible to learn device control when needed. Cheliout-Heraut and colleagues [77] reported abnormalities of somatosensory-evoked potentials in a sample of SMA children (type I and II). Yet, these abnormalities occurred far less frequent in SMA type II than in SMA type I. As somatosensory-evoked potential abnormalities were more pronounced in the lower limbs, the proposed tactor positions may not be feasible and thus adjusted individually. The same issue may apply to other types of diseases or injuries, e.g. in the case of spinal cord injury tactile perception on the legs is usually lost. Thus, in all cases, the system requires individually-tailored adjustments based on the sensory perception capabilities of patients.

Generalization of results may be limited with regard to the complexity of the navigation task performed in this study. The path did not require users to select all direction options. From the results of the copy-task, however, it appears unlikely that more errors would have occurred for a different path. Yet, future testing of the system should be performed with several different tasks over a longer period of time. In addition, a vivid environment, in which users need to react to changing settings, could provide useful insights in feasibility of tactual ERP-BCI systems under such, more realistic conditions. Finally, generalization may be limited as the third person perspective and the position tracking used in this study may have positively influenced navigation ability, e.g. estimation of distances. However, in a virtual environment it may be more difficult to estimate distances than in a real world setting. Thus, the benefit of position tracking and perspective may be negligible as compared to the benefit of navigating in a real environment.

However, in its current state the system bears some major drawbacks. (1) Some users reported that focusing on tactile stimulation was too demanding in a long navigation task. Thus, stimulation should be enhanced so that users perceive stimuli better. Furthermore, training in several sessions could be conducted to decrease users' workload. Halder and colleagues recently demonstrated, that performance with an auditory ERP-BCI can be improved with training [78]. Zickler and colleagues [10] demonstrated for visual ERP-BCIs that subjective workload of a naïve, severely motor impaired, potential end-user could be strongly decreased the more sessions were conducted, i.e. in his first session he rated workload rather high (49 of 100 on a linear scale) but decreased his rating to 15 in the last session. (2) The average time to deliver a command was roughly 28 seconds, ranging from 17.8 to almost 38.8 seconds. For effective wheelchair control, speed should be further enhanced, e.g. by implementing other dynamic stopping techniques or by increasing the signal-to-noise ratio of the recorded ERPs [7, 79]. As already addressed above, decreasing the off-time parameter of the system may also enhance speed. (3) The herein tested system is synchronous and not able to detect if a user wants to deliver a navigation command or perform any other task. For example, users may want to interrupt navigation and perform navigation-independent actions (e.g. communicating, reading, observing). It is thus inherently necessary to implement an asynchronous system that will account for such situations [80–82]. (4) Finally, we did not implement an option that rapidly allows for stopping the wheelchair. Once users delivered a movement command, they would hand over full control to the wheelchair, i.e. only its sensors could stop the wheelchair in case of an obstacle. Currently, if they delivered a wrong command, the wheelchair would still perform the action if the requested movement would not interfere with navigation barriers. Implementation of such correction method could be based on residual muscle activity or on other BCI signals in a hybrid approach (e.g., [49, 83–86]). This would possibly further reduce the amount of times, when shared control is necessary for intervention. However, already in our experimental setting, participants rarely needed shared control sensors and most of them had full control on the user side.