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People who have substantial experience with assistive technologies should be approached on the question of what a brain—computer interface-driven assistive technology would require if it was to be used in daily life. This provides valuable information regarding which aspects to consider when developing brain—computer interfaces for assistive technology. These include functionality, independence and ease of use Zickler et al.

However, it is difficult to judge something just by imagining its use without any experience of the technology itself Huggins et al. The authors illustrate which aspects need to be addressed and evaluated in translational studies with four questions: i is long-term independent use possible? I consider this section of the book to be of utmost importance because it not only pinpoints the necessity of translational studies, but also provides an algorithm on how to realize them and what measures may be used for evaluation.

In line with these demands, a brain—computer interface controlled by event-related potentials and implemented in a commercially available assistive technology has recently been evaluated by severely impaired end-users Zickler et al. Reliability and ease of learning were rated as being very good, whereas speed and aesthetic design were considered to be only moderate.

Obstacles for use in daily life included: i low speed; ii time needed to set up the system; iii handling complicated software; and iv the demanding strain that accompanies EEG recording. Although it is stressed throughout the book that brain—computer interfacing should allow independent use, as also emphasized by potential end-users, one has to admit that fully independent use will hardly be possible—or, it can be argued, necessary—because the potential end-users will be severely ill and in most cases in need of hour care.

The requirement for some action by a third person is unlikely to prevent brain—computer interfacing from being employed regularly. Additionally, it does not seem warranted to compare brain—computer interface performance to healthy motor output, as presented in this book; rather, performance must at least match the quality of existing assistive technologies. This is inevitably the yardstick with which potential end-users will actually judge their experience of brain—computer interfacing Zickler et al. With regard to speed, much has been achieved in recent times.

For example, Sellers et al. These examples suffice to illustrate the intensity of brain—computer interface research rendering the book in some respects already outdated. However, that is the case for every textbook that deals with a prosperous research topic and is a good indicator of its timeliness. In the final section, dissemination, ethical considerations and other potential targets for brain—computer interfacing are addressed, namely therapeutic application and uses for the general population.

The editors conclude by summarizing important problems for brain—computer interface research and development such as signal acquisition hardware; validation and dissemination; and reliability. Provided brain—computer interface research and development can bridge the translational and reliability gap, a promising future lies ahead for brain—computer interfacing technology, of which communication and control may be only one facet. A broader target population for communication and control is envisaged provided hybrid brain—computer interfaces that allow for more than one input signal Allison et al.

Clinical applications beyond substituting lost motor function are now also in the focus of research, specifically for stroke rehabilitation Kaiser et al. In combination with functional electric stimulation it is aimed at using brain—computer interface as a switch between stimulation of different muscles to restore functional grasp and elbow function in subjects with high spinal cord injury Rupp et al. Most recently, human control of neuroprosthetic devices reached a new performance level such that a female with tetraplegia due to neurodegenerative disease and an implant of channel intracortical microelectrodes in motor cortex was able to control a prosthetic limb with seven degrees of freedom in 3D space after 2 days of training Collinger et al.

These results further underline the need for translational concepts and studies to transfer technology from the laboratory to those people in need. Non-clinical applications are also on the rise. However, one has clearly to distinguish between serious approaches and thrilling advertisements for technology that do not realize brain but, rather, muscular control.

Still more science fiction than reality, one could also imagine integrating brain—computer interfaces in complex processes such as emergency management during mass events on the basis of complex event processing. Events sent to an event-cloud by exocortices of subscribed users, for example in a stadium, could be identified as emergency patterns by a smart space monitor. An appropriate action pattern would be transferred to the stadium visitors by means of smart sensors that would automatically trigger a specific behaviour in order to avoid congestion or a punchfest.

A wearable brain—computer interface would then directly send events to the cloud and provide information about successful resolution of the emergency or about the development of panic, which would then require further action Ehresmann et al. Anyone concerned with the future of neuroscience cannot ignore the implications and applications of the story laid out by Jonathan R.

Wolpaw and Elizabeth Winter Wolpaw in Brain computer interfaces. Principles and practice. Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide. Sign In or Create an Account. Sign In. Advanced Search. Article Navigation. Close mobile search navigation Article Navigation.

Volume Article Contents. Next, we introduce the notation. The attended stimulus during trial is. During stimulus presentation for trial stimulus is presented to the user. The function encodes that when the attended stimulus is presented, it has to be a target stimulus, if a different stimulus is presented then it must be a non-target stimulus. Let be the number of channels and the number of samples used per channel, then the dimensional EEG feature vector for stimulus during trial is denoted by.

This vector is projected towards when it is associated with a target stimulus, and towards if it is a non-target. The distribution of the projected EEG features has mean , depending on target or non-target and precision. The dimensional weight vector used to project the EEG is. Note that the additional term corresponds to the bias. The prior distribution on this weight vector has zero mean and precision. Furthermore, let be the matrix containing all the feature vectors for trial , one feature vector per column. Let be the matrix containing all the feature vectors recorded up till this point.

Toward Brain-Computer Interfacing by Guido Dornhege (ebook)

Finally, is the vector containing the target vs. Using the notation from above, the model is defined as follows. The term denotes a normal distribution with mean and precision. When we have a trained model, we can infer the probability that a specific stimulus is being attended by applying Bayes's rule: where we predict the stimulus with the highest likelihood.

We use the Expectation Maximization EM algorithm [50] to optimise and. The attended stimuli are unknown and have to be inferred during the expectation step. Optimizing is easier as it depends only on , thus direct maximum likelihood can be used. The resulting optimization process uses the following update equations. The update equation for can be seen as a weighted sum of all possible ridge regression classifiers, weighted by the probability that the labels used to train the classifier are correct given the previous estimate of.

The update for is the expected mean squared error between the projected feature vectors and the target feature vectors. Thus, equals the expected variance of the projected feature vectors. Finally, the precision is set to the inverse of the average squared classifier weight.


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Furthermore, we would like to stress that even though we train the classifier to detect the attended stimulus directly, a classifier which discriminates between target and non-target responses is embedded into the model. There is one big caveat when training a classifier without label information. It is impossible to control what the classifier actually learns, as the underlying algorithm tries to maximize the likelihood of the data under the current model.

Therefore, it is possible that the classifier learns to solve the exact opposite problem, i. However, it has been shown that there is a strong correlation between the data-log likelihood and the selection accuracy or the AUC [1]. To counter this problem, we adopted the following approach, which had originally been proposed in [1] , during the online experiments: We initialize five different classifier pairs. For each pair, we draw and we initialize one classifier with and one with.

Hence one classifier per pair can be expected to perform above chance level and one classifier will be below chance level in terms of AUC for labeling the individual feature vectors. After each trial, we perform five EM iterations per classifier. Due to the initialization, we expect that on average at least one classifier will learn to solve the desired task and one classifier will learn the opposite task.

Subsequently, we select the best classifier with respect to the data-log likelihood to predict the attended stimulus. After predicting the attended stimulus, we update the classifier pairs. Per pair, we select the classifier with the highest data-log likelihood. Let be its weight vector. Then we re-initialize the other classifier of the pair with.

This ensures that one classifier per pair will perform above chance level and one will perform below chance level. Using this strategy, we maximize the chance that at least one classifier would solve the task correctly. For correctness and reproducibility, we would like to mention that there was a minor mistake in the implementation of the log likelihood. We verified trough offline simulations that it did had not affected the experimental results. Furthermore, there are different options to select the best classifier, e.

When the classifier is used during an online experiment, it accumulates more and more unlabeled data to train on. As a consequence, the quality of the decoding model improves as more trials have been processed. Hence, a re-analysis of the stimuli of the previous trials may lead to different outcomes compared to the original online predictions. This so-called posthoc re-analysis of preceding trials can be done easily during the online experiment. Re-evaluating all previous trials in addition to the current trial allows us to measure accurately how successfully the classifier has adapted to the user.

Furthermore, this posthoc analysis strategy can provide an additional benefit to the user during the spelling task by accepting that the unsupervised classifier might initially make mistakes on some of the letters. These faulty decisions of the initial classifier might be revised during the subsequent posthoc re-analysis. The user can expect the posthoc classifier to correct the initial mistakes in the output during the course of the online experiment. The data from the original AMUSE study [42] , which comprises 21 subjects, was used in an offline analysis to determine the hyperparameters of the classification methods.

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The pre-analysis showed, that the methods performed stable, and with good results for a large range of values. For the unsupervised method, we opted for 5 classifier pairs, and the number of EM updates per trial was fixed to 5 as well. Additionally, we chose to initialize to 1 and to During the experiments, the value of the regularization parameter was limited to at most to prevent the classifier from collapsing on the degenerate solution of a vector of zeros.

This is a practice that was suggested in the original paper [1]. The number of stimulus iterations per trial was not determined in a data driven approach. The number of trials for both the calibration block and the online blocks was This value was selected based on our prior experience with the supervised method. More than 30 trials of calibration data would not lead to a significant further improvement of the classifier on the grand average of the original AMUSE data.

For both the unsupervised and the supervised recordings, very similar ERP responses are observed in the grand average analysis left and right plots of Fig. This is a first indicator that the online performance differences between the methods see below are not caused by differences in the recorded data. In both conditions, typical attention-related and class-discriminative differences can be discerned: a fronto-central negativity between and ms post stimulus and a positivity from ms onwards. Compared to the original AMUSE setup, the target- and non-target ERP-responses of fast auditory ERP paradigms were reproduced in the current study — despite the minor changes in the experimental setup.

Top row: Responses evoked by target blue and non-target green stimuli for channels Cz thick and F5 thin. Middle row: Scalp plots visualizing the mean target t and non-target nt responses within five selected time intervals see grey markings of the top row from ms to ms post stimulus. Bottom row: Scalp plots visualizing the spatial distribution of class-discriminant information, expressed as the signed and scaled area under the receiver-operator characteristic curve ssAUC.

In our experiments, two flawless trials are needed to spell a symbol correctly. We begin by presenting the trial-wise selection accuracies from the online experiment in Fig. For each subject and each condition, the accuracy is given per block. For each user and the grand average GA , the performances of three experimental blocks are given. Chance level performance is at. Top plot : Online performance of the three blocks per user classified by the supervised LDA approach.

Per subject, the classifier had been pre-trained on calibration data not shown and kept fix for all three blocks. Middle plot : Online performance of blocks controlled by the unsupervised classifier. The unsupervised classifier had been initialized randomly before each individual block three times per subject. Bottom plot : performance of the posthoc re-analysis method for the unsupervised blocks.

The posthoc classifier, too, had been initialized randomly before each block. Averaged over all experimental runs, which comprises 30 experimental blocks 10 users times three blocks with 30 trials per block, the pre-calibrated baseline method supervised obtains a selection accuracy of Due to the fixed classifier, the performance is relatively stable over the three supervised blocks. Increased fatigue had been reported by a number of participants for the last blocks. This may have lead to the slight performance drop from the second to the third last block. Based on the supervised classifier alone, it can not be explained how fatigue might have influenced the classification performance.

We present two hypotheses: first, the changed mental state lead to non-stationarity in the EEG, but the actual attention task is still performed well by the fatigued users. As a consequence, the fixed classifier has more difficulty to decode the trials of the last block, as the non-stationarity in the EEG disturbs the decoding e. Second, the class-discriminative information contained in the last block might be reduced due to attention deficits of the users, which would result in a reduced SNR due to a less informative signal component.

We will show later, by simulating an extended experimental session with the unsupervised method, that the SNR is not reduced and that the data can be decoded reliably. In the short online blocks, the randomly initialized unsupervised method did not reach the performance level of the supervised classifier. But there is a large amount of variability between the different users. The best result was obtained during the first unsupervised block of user , where only the second out of 30 trials was faulty.

In addition, the average unsupervised performance is increased from the first to the second block. On the individual level, a substantial amount of variability between unsupervised blocks of the same user is observed. Under the hard testing conditions re-initializing the unsupervised classifier to random values before the start of each block , the unsupervised classifier performs at chance level at the beginning of each block. The performance improves dramatically towards the end of a block, resulting in 7.

In the posthoc condition, the classifier performance increases to an average of As a comparison, the pre-trained supervised classifier manages to spell To give the reader a feeling of the spelling quality of the unsupervised approach, Fig. In the first two blocks, the posthoc classifier was able to revise a substantial number of symbols which had been predicted erroneously during the course of the experiment.

Per block, the top line represents the desired text, the middle line displays text produced online by the unsupervised classification. Text predicted by the posthoc re-analysis at the end of the block is shown at the bottom line. Two trials are needed to determine a symbol.

Individual selection errors wrong trials of both methods are marked by black squares directly below each symbol. Please note that the classifier was re-initialized randomly at the beginning of each block. Unsupervised learning is a significantly more difficult problem than solving the decoding task with a supervised classifier.

This was amplified by re-initializing the unsupervised classifiers randomly at the beginning of each block. As a result, some blocks could not be decoded properly by the unsupervised methods, while the supervised classifier succeeded to do so. As an example, the performance was rather poor in the third unsupervised block of subject nbf and even the posthoc re-analysis was not able to correct the output to a human-readable level within these 30 blocks. When we applied the supervised classifier to this block in an offline analysis , it was able to perform well on this block. If, however, the information content is rather high, then the data of 15 trials is sufficient to obtain a good solution unsupervised.

The second block of subject nbf can be taken as an example — here the selection accuracy of the posthoc re-analysis was equal to the best supervised result for nbf. To judge the value of the three methods we should not be restricted to the spelling accuracy on short blocks.

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The invested amount of time is an important factor, especially for patient applications. At the moment of the posthoc re-analysis e. While the calibration recording cannot result in any usable text output, the unsupervised block can.

Brain Computer Interfaces

We are aware, that this rate is not yet enough to communicate in practical situations. On the other hand, the remedy is simple: as we will show later on in a simulated time-extended experiment, most of the errors can be sorted out by posthoc if the spelling duration is prolonged. Now we return to the trial-based performance, and analyze the dynamic behavior within each of the 30 online blocks. The unsupervised method undergoes a constant learning process during the online usage of the BCI. It reveals a so-called warm-up period even on a single-subject basis Fig.

This period explains the reduced performance compared to supervised: unsupervised makes more mistakes during the beginning of each block than at the end. Time is on the horizontal axis, while the lines represent users. The order of the users equals that of Fig. For each trial and user, a green square indicates an accurate selection, a black one marks an error. Clearly, the unsupervised classifier commits most erroneous decisions shortly after its random initialization at the beginning of each novel block. In the majority of cases users were able to effectively control the BCI by the end of a block.

Hence, it is an important question, how long an average user takes to obtain control over the BCI with the unsupervised approach. The probability to do so by guessing is only. The exact point in time where the user takes control of the BCI for the first time is defined as the first trial of the first sequence of 3 error-free trials. By applying this definition, only three runs ended without a user reaching control. These were the runs of and in the first unsupervised block and the run of during the third unsupervised block.

For the other runs, the average number of trials necessary to achieve control was. Two runs resulted in control in the very first trial. As we discussed before, the post-hoc re-analysis re-applies the final classifier to all trials after processing the entire block. In the actual experiment, post-hoc was also used to compute an updated estimate after each trial.

Finally, during seven out of 30 blocks, the post-hoc method was able to present and error-free decoding of the entire block. The block-wise selection accuracy for post-hoc is shown in Fig.

The Berlin Brain-Computer Interface: Progress Beyond Communication and Control

Unfortunately, for the three blocks that did not result in control in the unsupervised setting, the post-hoc re-analysis failed too. The data displayed stems from the same blocks as in Fig. With the exception of three difficult blocks first blocks of users nbe and jh , and third block of user nbb the posthoc re-analysis obviously outperforms the original online performance gained by the unsupervised method see Fig.

It effectively corrected most initial mistakes at the beginning of each block, thus recovering communication from the very first trial on. Both unsupervised methods online and posthoc had trouble selecting a good performing classifier for the three difficult blocks. For two of these three cases, we received specific comments by the users. User nbe reported after the first online block that during this block, which happened to be with the unsupervised classifier, she had trouble ignoring one very salient tone front-left. In the questionnaire at the end of the session she reported, that this problem did not persist during following blocks as she had found an internal strategy to concentrate better on target tones.

User jh reported, that during his first online spelling block which happened to be with the unsupervised classifier he had trouble ignoring one very salient tone front-right. However, he got used to it or found a different mental strategy and reported that the problem was solved in the following blocks.

Nothing was reported by user nbb which could explain the performance break-down in the third unsupervised block which happened to be the last block overall for this user. In the next section we will demonstrate by means of a simulated experiment of an extended online spelling session that even these blocks contain enough information to allow for reliable decoding of the EEG — with both the unsupervised and the supervised methods. Hence, the decoding problem for the unsupervised approach is not caused by non-informative EEG signals, but rather by the combination of a relatively short block duration and a rather low signal to noise ratio.

This combination prolongs the warm-up period of the unsupervised classifiers. In general, the less data available the harder it is to learn without label information, and this is amplified when the data has a low signal to noise ratio. As mentioned above, it is interesting to evaluate the performance during spelling sessions of an extended duration.


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  • For this purpose we emulated a long spelling session by concatenating EEG data of the three supervised and the three unsupervised blocks per subject in chronological order. This allows the unsupervised method to improve the model by using much more data compared to the true online experiment.

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    Furthermore, in this setting, the post-hoc classifier has seen the full data of all six blocks before it re-analyzed all trials. The grand average result for the supervised, unsupervised and post-hoc methods are compared in Fig. Here we see that during the first block of 30 trials the supervised method outperforms the unsupervised and post-hoc approaches.

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