Real-time brain computer interface using imaginary movements
© El-Madani et al. 2015
Received: 22 July 2015
Accepted: 9 October 2015
Published: 28 October 2015
Brain Computer Interface (BCI) is the method of transforming mental thoughts and imagination into actions. A real-time BCI system can improve the quality of life of patients with severe neuromuscular disorders by enabling them to communicate with the outside world. In this paper, the implementation of a 2-class real-time BCI system based on the event related desynchronization (ERD) of the sensorimotor rhythms (SMR) is described.
Off-line measurements were conducted on 12 healthy test subjects with 3 different feedback systems (cross, basket and bars). From the collected electroencephalogram (EEG) data, the optimum frequency bands for each of the subjects were determined first through an exhaustive search on 325 bandpass filters. The features were then extracted for the left and right hand imaginary movements using the Common Spatial Pattern (CSP) method. Subsequently, a Bayes linear classifier (BLC) was developed and used for signal classification. These three subject-specific settings were preserved for the on-line experiments with the same feedback systems.
Six of the 12 subjects were qualified for the on-line experiments based on their high off-line classification accuracies (CAs > 75 %). The overall mean on-line accuracy was found to be 80%.
The subject-specific settings applied on the feedback systems have resulted in the development of a successful real-time BCI system with high accuracies.
KeywordsBrain computer interfaces (BCI) Electroencephalogram (EEG) Movement imagery (MI) Event-related desynchronization (ERD) Feedback systems Bayes linear classifier (BLC)
Brain Computer Interface (BCI) - the method of transforming mental thoughts and imagination into actions has been a very interesting and challenging research topic of neuroscience in recent years. The primary reason for such high interest is that it helps to improve the quality of life of patients with severe neuromuscular disorders. BCI based systems enable such patients to communicate with the outside world even without the output channels of peripheral nerves and muscles [1–5]. They can be used for the purpose of communication (e.g. spelling device) [6, 7], interaction with external devices (e.g. controlling a wheelchair) [8, 9], rehabilitation [10, 11] and/or for monitoring the mental states [12, 13].
Various types of non-invasive BCI systems have been developed using different types of brain signals (electroencephalograms (EEGs)). Commonly used EEG signals include the event-related P300 potentials [14–18], steady-state visual evoked potentials (SSVEPs) [19–23], and the motor imagery (MI)-related rhythms [24–30]. In these, the P300 potentials and SSVEPs are evoked by external stimuli and the MI rhythms are voluntarily modulated by the subjects.
It has been well studied that the imagination of movements of left and right hands results in the event-related desynchronizing (ERD) of the sensory motor rhythms (SMR) in the contralateral sensorimotor areas and event related synchronization (ERS) on the ipsilateral side [31, 32]. The corresponding distinguishable features in the EEG signals can be used to design MI-based BCI systems [24–30]. Many BCI studies have reported good offline results with high accuracies. However, a BCI-system becomes interesting when it is able to work in real time. In this paper, a real-time MI-based BCI system was developed in which the imagination of the movements of left and right hands were tested resulting in a system with 2-class output [33–35].
In the DTU-BCI set-up shown in Fig. 1, 28 EEG surface electrodes placed on (and around) the motor cortex has been used [7, 36, 37]. Furthermore, EMG electrodes were placed on both the arm wrists during the offline measurements to verify the passivity of the arm muscles. Twelve healthy test-subjects (seven males and five females at an average age of 23±2.6 years) took part in this study. None of them had previous history of neurological diseases or disorders that may influence the experiments. Each participant went through an Edinburgh Handedness test . The handedness test showed that six males and all females were right handed, and only one male was left-handed. Each test-subject was given instruction about the measurement procedures and protocols before the first session. All subjects received remuneration for their participation.
Several studies have shown that the ERD signals can be localized at the sensorimotor cortex . However, the SMR waves in the EEG are generally weak and it is impossible to classify the raw EEG directly [1, 40]. Therefore, EEG data were processed in order to extract the relevant features of the SMR which are distinguishable to be used as different control signals in a BCI set-up.
We used the Common Spatial Patterns (CSP) algorithm to extract the features from the collected EEG as it has been shown to be very efficient in extracting the features with 2-class BCI systems based on movement imagination [7, 34]. In our approach, the CSP filter was found from the labeled offline data − a large matrix (V) of dimension N×T, where N(=28) is the number of channels and T is the number of samples in each channel (depends on the window length). The data matrix contains 160 mixed trials, 80 trials of right hand imaginary movements (r−trials) and 80 trials of left hand imagery movements (l−trials).
In order to perform an online classification using the equations above, the covariance matrix Σ and the two class means μ l and μ r were to be known. These were calculated from the offline data using 3×5-fold cross-validation. In the online BCI, no cross-validation processes were applied. Instead, data from a sliding window was first bandpass filtered and then CSP-filtered using parameters extracted from the offline measurements. Features of each online data segments were classified using the BLC. Each classification was then fed in to a voting system that gave the final classification accuracy (CA) after e.g., 8 data segments. The following parameters were used in the voting system: segment length range of 0.5−1.5 sec, the window overlapping of 90 and 95 %, and the number of segments of 6−15. All test parameters were individually selected using a graphical user interface.
Though the estimated parameters from offline analysis has been used in the online measurements, the data from the online measurements could show considerable variation . These variations may be due to non-stationeries caused by the small changes in electrode positions, drying conductive gel or electrodes with high impedances, brain plasticity, especially after several sessions, or variations in the cognitive state of the user, e.g. motivation, attention etc.
The red dotted line in the figure represents the pdf of (D r −D l ) when performing imaginary left hand movements and the blue solid line represents the pdf of (D r −D l ) when performing imaginary right hand movements. The vertical green line is the decision threshold (=0). It is worth noting, that even though the subject-specific CSP filter maximizes the separation between the two classes, there will always be an overlap (due to the nature of the distribution). And because of the variations mentioned earlier, the means of (D r −D l ) for left and right imaginary movements are unlikely to be equidistant to the threshold. Therefore, this classifier may result in lower classification accuracy in online BCI.
This consists of a blue cross in the middle of the screen, and two grey bars (the left goal and the right goal), on each side of the screen (Fig. 3 a). The objective is to move the cross to the left or the right goal, by performing imaginary left or right hand tapping, respectively. At the beginning of each trial, one of the two goals gets red and becomes the target goal. The cross moves one step to the side that corresponds to the online command. In other words, if the final classification decision is left (l), the cross moves one step to the left. If right (r), then the cross moves one step to the right. The cross stays in the same position if the command is “no classification”. Each session consist of ten pseudo-random trials, five left and five right. After each trial, a 5-s pause is given. Once the cross has reached one of the goals, the system saves the selection and ends the trial.
This system consists of a blue ball at the top of the screen, and two goals at the bottom (Fig. 3 b). When the trial begins, the ball starts falling with a constant speed. The objective is to move the ball, by means of imaginary hand movements, to the target goal (the one with red color). The ball speed can be adjusted before running the measurement. This system also had the same trial construction as the cross paradigm. Each test-subject went through this paradigm several times, and the ball speed has been varied for some of the subjects. Table 4 lists the mean trial durations (MTDs) and CAs of the subjects performing the Basket Feedback.
There is no object to move in this feedback system. The feedback consists of two bars, one on each side of the screen. These bars are empty, and are filled gradually after each final classification (Fig. 3 c). The trial begins with an arrow appearing on the middle, instructing the test subject, which imaginary movement to perform, i.e. which bar to fill up. If the arrow is pointing to the right, then the test subject has to imagine right hand movement in order to fill up the right bar, and vice versa. It takes 10 steps to fill a bar up. Once one of the bars is filled, a selection is made. Therefore, a trial has a minimum duration of ten steps and maximum duration of 19 successful steps. The step duration is the time between two final classifications.
Results and discussions
The most important experiments and the results are presented in this section. The results are based on about 100 h of measurements and many hours of measurements preparation.
The preliminary offline measurements were carried out to deselect BCI illiterates and to calculate the filters and other parameters for the test subjects [45, 46]. Each offline calibration measurement consisted of 80 left and 80 right trials in a pseudo-random order.
The ERD plots show larger power in C4 than in C3 during left hand movement imagination. On the other hand, C3 has large power than C4 during right hand movement imagination. Recall that C3 and C4 are located on the left and right sensorimotor cortex hand areas, respectively. Recall also, that the left sensorimotor cortex is responsible for the contralateral (right) body movements, and vice versa. The ERD plots confirm this phenomenon during movement imagination. It is worth noting, that the power is not uniformly distributed along the frequency axis. Figure 4 shows that the most significant power changes occur at 5–12 Hz. Another active frequency band is 18–23 Hz. When comparing across the subjects, we do not find precisely same patterns regarding active frequency bands. This finding confirms our objective to select subject-specific frequency bands in online BCI.
Optimal offline results
The offline results of all test-subjects. The optimal accuracies listed in the right column are found by analyzing the offline data using different bandpass filters. The optimal frequency ranges listed in the middle column are the bandpass filters that result in optimal classification with the optimal accuracy. While recording the EEG data, the electrode impedance was kept under 5 k Ω
Optimal frequency range (Hz)
Subject categories according to the offline analysis on CAs
1, 3, 5, 6, 11
2, 4, 7
9, 10, 12
This shows that half of the subjects had accuracies below 75 %. Since well-functioning online BCI requires relatively high accuracies, the subjects with accuracies above 75 % have been considered (six subjects: 2, 4, 7, 9, 10 and 12) for online measurements.
Results from the online sessions (subject 10 did not participate in Bar feedback sessions due to personal reasons). In Basket feedback, the ball speed is predefined, and the subjects have no influence on the trial time. Therefore the standard deviations of the MTD’s for this case is omitted
24.99 ± 17.04
13.03 ± 03.49
17.34 ± 08.14
06.54 ± 00.42
16.91 ± 17.46
05.35 ± 00.14
21.02 ± 10.80
08.72 ± 04.19
16.52 ± 08.84
13.44 ± 04.49
06.78 ± 00.38
17.99 ± 10.51
79.10 ± 15.20
12.47 ± 01.89
81.49 ± 10.10
08.01 ± 01.48
80.01 ± 09.01
Average ball speed for each subject
Mean ball speed (s)
This feedback system was tested on all online subjects, except subject 10 (could not participate due to personal reasons). In Table 3, the mean accuracies and trial durations for the Bar Feedback are given. Subjects 4, 7, 9, and 12 accomplished the sessions with a mean CA between 77.50 and 100.00 %. Subject 2’s mean CA was higher than for the other two feedback systems, but still significantly lower than the other subjects. By observing the MTDs, it is found that the times are substantially lower than the trial durations for the two other feedback systems.
Online results analysis
From the results in Table 3, we can see that, (i) Although all three feedback systems resulted in more or less similar CAs, Cross feedback had shown significantly large deviations, (ii) The MTDs differ significantly from each other; 18.31, 12.35 and 8.08 s, respectively for the Cross, Basket and Bar feedbacks, and (iii) The cross feedback had the highest standard deviation of the mean trial duration compared to the two other paradigms. These findings indicate that the Cross feedback is not as stable as the other two paradigms. It is however possible to refine this paradigm e.g. by reducing the distance to the goals in order to reduce the MTD and at the same time improve the stability.
The learning effect
Trial duration vs. accuracy
During the online measurements with the three feedback systems, few implementation errors were registered, and potential refinements were suggested. A general problem was detected in all three paradigms. At the beginning of each trial (except the first trial), the first few segments from the sliding window contained data from the previous trial due to overlapping. Therefore these ‘old’ data could affect the first few classifications of the new trial and in the worst case affect the first final classification of each trial (recall the voting process). Another potential improvement concerning all three feedback systems is to illustrate the stepwise moves (of the cross, ball and the bar fill) as continuous moves. Even though this change is only visual, it may minimize confusion, and prevent unconscious step-synchronized body movements. Finally the last common improvement is to instruct the subjects about the feedback paradigms (Cross and Basket: red target, Bars: direction arrow) at least few seconds before each trial. In the current paradigms, it was introduced concurrently with the beginning of the trial. Thus, the subjects spent up to couple of seconds to react on these commands while the segments from the sliding window were classified, possibly wrongly. In the following three subsections, specific improvements and changes of the paradigms are suggested.
Improvements of cross
Analysis of results for target distance equal to four steps
in accuracy (%)
Improvements of basket
Since Basket feedback has limited MTD, reducing the target distance (number of steps to the side borders in this case) will have less effect. However, the large number of online measurements using Basket indicates that a distance of 20 steps is too long. Therefore, a distance reduction may result in a reduction in MTD.
Improvements of bars
Some subjects experienced, that the command arrow was thin and unclear. Another problem occurred, when the filling difference between the left and right bars were small. For instance, if both left and right bars were filled up with nine steps at the end of the trial, then the last step decides the class of the trial. Because the figure clears when the decision is made, the user will not be able to detect the decision. This problem can be solved by viewing the decision in the beginning of the 5-second pause.
EMG contribution in online BCI
Besides visually inspecting the subjects during the measurements, the EMG recorded during the offline measurements were used to ensure that the ERDs of the SMR were due to movement imagination rather than due to real muscle movements. The spectrogram of the EMG data did not show a significant power change that could indicate that the subject was making a real hand flexion. It was found that the EMG analysis was in accordance with the visual inspection, which showed that EMG activity was negligible for all subjects.
During the online measurements, EMG was not measured but was only visually inspected. However, real and imaginary movements do not result in same EEG spatial patterns. Therefore during online BCI, a CSP filter that is calculated from offline data with minimal EMG activity will not result in optimal feature extraction if real movements were performed. Consequently, real movements may probably lead to bad classification. This hypothesis was tested during few online sessions: the subject was told to perform real hand movements instead of imaginary movements. Many of the resulted classifications were incorrect, and the feedback showed a more or less random path of the cross.
This paper has focused on the challenges of developing a real-time BCI system using the desynchronization phenomenon of the SMR. The first part was to conduct offline calibration measurements to determine the optimal subject-specific parameters to use in the online part. Offline data were processed using CSP to extract the relevant features. BLC was trained using the labeled features from each data. Twelve test-subjects participated in the offline measurements and six of them qualified to participate in the online measurements. Three online feedback paradigms were designed and used (cross, basket, and bars) in this work. While all three paradigms resulted in similar CAs, the results of cross indicated instability. This was reflected by prolonged trial time, large standard deviation of the trial times, and the large deviation of the CAs. The overall online CA was 80 %. It was found, by studying possible improvements of cross, that reducing the target distance from ten steps to four steps resulted in 70 % reduction of MTD. This improvement will only reduce the CA by 2 %.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.
- Dornhege G, Millan J, Hinterberger T, McFarland DJ, Müller KR. Toward brain-computer interfacing. Cambridge: MIT Press; 2007.Google Scholar
- Wolpaw JR, Birbaumer N, Heetderks WJ, McFarland DJ, Peckham PH, Schalk G, et al.Brain-computer interface technology: a review of the first international meeting. IEEE Trans Neural Syst Rehabil Eng. 2000; 8(2):164–73.View ArticleGoogle Scholar
- Birbaumer N, Ghanayim N, Hinterberger T, Iversen I, Kotchoubey B, Kübler A, et al.A spelling device for the paralysed. Nature. 1999; 398:297–8.View ArticleADSGoogle Scholar
- Kübler A, Birbaumer N. Brain-computer interfaces and communication in paralysis: extinction of goal directed thinking in completely paralysed patients?Clin Neurophysiol. 2008; 119:2658–66.View ArticleGoogle Scholar
- Kübler A. Brain-computer interfacing: science fiction has come true. Brain. 2013; 136:2001–4.View ArticleGoogle Scholar
- Sellers EW, Donchin E. A P300-based brain-computer interface: initial tests by ALS patients.Clin Neurophysiol. 2006; 117:538–48.View ArticleGoogle Scholar
- Wolpaw JR, Birbaumer N, Pfurtscheller G, Mcfarland DJ, Vaughan TM. Brain-computer interfaces for communication and control. Clin Neurophysiol. 2002; 6:767–91.View ArticleGoogle Scholar
- Del R Millan JJ, Galan F, Vanhooydonck D, Lew E, Philips J, Nuttin M. Asynchronous non-invasive brain-actuated control of an intelligent wheelchair. In: Conf. Proc. of the 31st IEEE Eng. Med. Biol. Soc. USA. Minnesota: Hilton Minneapolis: 2009. p. 3361–4.Google Scholar
- Mohebbi A, Engelsholm SK, Puthusserypady S, Kjaer TW, Thomsen CE, Sorensen HBD. A brain computer interface for robust wheelchair control application based on pseudorandom code modulated visual evoked potential. In: Proc. of the 37th Intl. Conf. of the IEEE Eng. Med. Biol. Soc. Milan, Italy: 2015.Google Scholar
- Ali A, Puthusserypady S. A 3D learning playground for potential attention training in ADHD: A brain computer interface approach. In: Proc. of the 37th Intl. Conf. of the IEEE Eng. Med. Biol. Soc. Milan, Italy: 2015.Google Scholar
- Daly JJ, Wolpaw JR. Brain-computer interfaces in neurological rehabilitation. Lancet Neurol. 2008; 7:1032–43.View ArticleGoogle Scholar
- Blankertz B, Tangermann M, Vidaurre C, Fazli S, Sannelli C, Haufe S, et al.The Berlin brain-computer interface: non-medical uses of BCI technology. Front Neuroscience. 2010; 4(Article 198):1–17.Google Scholar
- Müller KR, Tangermann M, Dornhege G, Krauledat M, Curio G, Blankertz B. Machine learning for real-time single-trial EEG-analysis: from brain-computer interfacing to mental state monitoring. Jl Neurosci Methods. 2007; 167(1):82–90.View ArticleGoogle Scholar
- Serby H, Yom-Tov E, Inbar GF. An improved P300-based brain-computer interface. IEEE Trans Neural Syst Rehabil Eng. 2005; 13(1):89–98.View ArticleGoogle Scholar
- Lugo ZR, Rodriguez J, Lechner A, Ortner R, Gantner IS, Laureys S, et al.A vibrotactile p300-based brain-computer interface for consciousness detection and communication. Clin EEG Neurosci. 2014; 45(1):14–21.View ArticleGoogle Scholar
- Piccione F, Giorgi F, Tonin P, Priftis K, Giove S, Silvoni S, et al.P300-based brain computer interface: reliability and performance in healthy and paralysed participants. Clin Neurophysiol. 2006; 117(3):531–7.View ArticleGoogle Scholar
- Farwell LA, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroenceph Clin Neurophysiol. 1988; 70(6):510–23.View ArticleGoogle Scholar
- Bayliss JD. Use of the evoked potential P3 component for control in a virtual apartment. IEEE Trans Neural Syst Rehabil Eng. 2003; 11(2):113–6.MathSciNetView ArticleGoogle Scholar
- Vilic A, Kjaer TW, Thomsen CE, Puthusserypady S, Sorensen HBD. DTU BCI speller: an SSVEP-based spelling system with dictionary support. In: Proc. of the 35th IEEE Eng. Med. Biol. Soc. Osaka, Japan: 2013. p. 2212–5.Google Scholar
- Ortner R, Allison B, Korisek G, Gaggl H, Pfurtscheller G. An SSVEP BCI to control a hand orthosis for persons with Tetraplegia. IEEE Trans Neural Syst Rehabil Eng. 2011; 19(1):1–5.View ArticleGoogle Scholar
- Leow R, Ibrahim F, Moghavvemi M. Development of a steady state visual evoked potential (SSVEP)-based brain computer interface (BCI) system. In: Intl. Conf. on Intell. and Adv. Syst. Kuala Lumpur: 2007. p. 321–4.Google Scholar
- Liavas AP, Moustakides GV, Henning G, Psarakis EZ, Husar P. A periodogram-based method for the detection of steady-state visually evoked potentials. IEEE Trans Biomed Eng. 1998; 45(2):242–8.View ArticleGoogle Scholar
- Cheng M, Gao X, Gao S, Xu D. Design and implementation of a brain-computer interface with high transfer rates. IEEE Trans Biomed Eng. 2002; 49(10):1181–6.View ArticleGoogle Scholar
- Wolpaw JR, McFarland DJ. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc Nat Acad Sci. 2004; 17:849–54.Google Scholar
- McFarland DJ, Wolpaw JR. Sensorimotor rhythm-based braincomputer interface (BCI): feature selection by regression improves performance. IEEE Trans Neural Syst Rehabil Eng. 2005; 13(3):372–9.View ArticleGoogle Scholar
- Yue J, Zhou Z, Jiang J, Liu Y, Hu D. Balancing a simulated inverted pendulum through motor imagery: an EEG-based real-time control paradigm. Neurosci Lett. 2012; 524(2):95–100.View ArticleGoogle Scholar
- Friedricha E, Schererb R, Neuper C. Long-term evaluation of a 4-class imagery-based brain-computer interface. Clin. Neurophysiol. 2013; 124(5):916–27.View ArticleGoogle Scholar
- Hazrati M, Erfanian A. An online EEG-based brain-computer interface for controlling hand grasp using an adaptive probabilistic neural network. Med Eng Phys. 2010; 32(7):730–9.View ArticleGoogle Scholar
- Faller J, Vidaurre C, Solis-Escalante T, Neuper C, Scherer R. Auto-calibration and recurrent adaptation: towards a plug and play online ERD-BCI. IEEE Trans Neural Syst Rehabil Eng. 2012; 20(3):313–9.View ArticleGoogle Scholar
- Iversen IH, Ghanayim N, Kübler A, Neumann N, Birbaumer N, Kaiser J. A brain-computer interface tool to assess cognitive functions in completely paralyzed patients with amyotrophic lateral sclerosis. Clin Neurophysiol. 2008; 119(10):2214–23.View ArticleGoogle Scholar
- Decety J. The neurophysiological basis of motor imagery. Behav Brain Res. 1996; 77(1-2):45–52.View ArticleGoogle Scholar
- Pfurtscheller G, Lopes DSF. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol. 1999; 110(11):1842–1457.View ArticleGoogle Scholar
- Royer A, He B. Goal selection versus process control in a brain-computer interface based on sensorimotor rhythms. Jl Neural Eng. 2009;6(1). doi:10.1088/1741--2560/6/1/016005.
- Blankertz B, Losch F, Krauledat M, Dornhege G, Curio G Müller KR. The Berlin brain-computer interface: accurate performance from first-session in BCI-Naïve Subjects. IEEE Trans on Biomed Eng. 2008; 55(10):2452–62.View ArticleGoogle Scholar
- Vuckovic A, Sepulveda F. Quantification and visualisation of differences between two motor tasks based on energy density maps for brain-computer interface applications. Clin Neurophysiol. 2008; 119(2):446–58.View ArticleGoogle Scholar
- Kübler A, Müller KR. An introduction to brain-computer interfacing. Cambridge: MIT Press, p. 2007.Google Scholar
- El-Madani A. Introduction to brain computer interface. Study report. Denmark: Dep. Elec. Eng., DTU, Lyngby;2009.Google Scholar
- Oldfield RC. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia. 1971; 9(1):97–113.View ArticleGoogle Scholar
- Müller-Gerking J, Pfurtscheller G, Flyvbjerg H. Designing optimal spatial filters for single-trial EEG classification in a movement task. Clin Neurophysiol. 1999; 110(5):787–98.View ArticleGoogle Scholar
- Blankertz B, Tomioka R, Lemm S, Kawanabe M, Müller KR. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Sig Process Mag. 2008; 25(1):41–56.View ArticleADSGoogle Scholar
- Fukunaga K. Random vectors and their properties. In: Introduction to statistical pattern recognition. New York: Morgan Kaufmann Publishers: 1990. p. 10–35.Google Scholar
- Pedro D, Michael P. On the optimality of the simple Bayesian classifier uner zero-one loss. Mach Learn. 1997; 29:103–30.MATHView ArticleGoogle Scholar
- Jonsson M.Brain computer interface. MSc. Thesis. Denmark: Dep. Elec. Eng., DTU; 2008.Google Scholar
- Blumberg J, Rickert J, Waldert S, Schulze-Bonhage A, Aertsen A, Mehring C. 2007. Adaptive classification for brain computer interfaces, Vol. 1. France: Med. Biol. Soc. Conf. Lyon.Google Scholar
- Dickhaus T, Sannelli C, Müller KR, Curio G, Blankertz B. Predicting BCI performance to study BCI illiteracy. Bio. Med. Centr. Neuroscience. 2009; 10(Suppl 1):84. doi:10.1186/1471-2202-10-S1-P84.Google Scholar
- Vidaurre C, Blankertz B. Towards a cure for BCI illiteracy. Brain Topogr. 2010; 23(2):194–8.View ArticleGoogle Scholar