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Patients in the completely locked-in state have no means of communication and they represent the target population for brain-computer interface research in the last 15 years. Although different paradigms have been tested and different physiological signals used, to date no sufficiently documented completely locked-in state patient was able to control a brain-computer interface over an extended time period. We introduce Pavlovian semantic conditioning to enable basic communication in completely locked-in state. This novel paradigm is based on semantic conditioning for online classification of neuroelectric or any other physiological signals to discriminate between covert (cognitive) 'yes' and 'no' responses. The paradigm comprised the presentation of affirmative and negative statements used as conditioned stimuli, while the unconditioned stimulus consisted of electrical stimulation of the skin paired with affirmative statements. Three patients with advanced amyotrophic lateral sclerosis participated over an extended time period, one of which was in a completely locked-in state, the other two in the locked-in state. The patients' level of vigilance was assessed through auditory oddball procedures to study the correlation between vigilance level and the classifier's performance. The average online classification accuracies of slow cortical components of electroencephalographic signals were around chance level for all the patients. The use of a non-linear classifier in the offline classification procedure resulted in a substantial improvement of the accuracy in one locked-in state patient achieving 70% correct classification. A reliable level of performance in the completely locked-in state patient was not achieved uniformly throughout the 37 sessions despite intact cognitive processing capacity, but in some sessions communication accuracies up to 70% were achieved. Paradigm modifications are proposed. Rapid drop of vigilance was detected suggesting attentional variations or variations of circadian period as important factors in brain-computer interface communication with locked-in state and completely locked-in state.

The objective was to extract, quantify and characterize the uterine magnetomyographic (MMG) signals that correspond to the electrophysiological activity of the uterus.Transabdominal MMG recordings with high spatial-temporal resolution were performed with the use of the 151 non-invasive magnetic sensor system. The extraction, quantification and characterization procedures were developed and applied to representative MMG signals that were recorded from a pregnant woman at regular intervals starting at 37 weeks of gestation until the subject reached active labor.Multiple MMG recordings were successfully performed on the subject before she went into active labor. The extracted MMG burst activity showed a statistically significant correlation (r=0.2; p0.001) with the contractile events perceived by mothers. The time-frequency analysis of the burst activity showed a power shift towards higher-frequency at 48 h before the subject went into active labor as compared to earlier recordings. Further there was a gradual increase in the synchrony in the higher-frequency band as the subject reached close to active labor.The non-invasive recording of the magnetic signals of pregnant uterus with high spatial-temporal resolution can provide an insight into the preparatory phase of labor and has the potential of predicting term and preterm labor.

Objective: The current study evaluates the efficacy of a P300-based brain–computer interface (BCI) communication device for individuals with advanced ALS. Methods: Participants attended to one cell of a N N matrix while the N rows and N columns flashed randomly. Each cell of the matrix contained one character. Every flash of an attended character served as a rare event in an oddball sequence and elicited a P300 response. Classification coefficients derived using a stepwise linear discriminant function were applied to the data after each set of flashes. The character receiving the highest discriminant score was presented as feedback. Results: In Phase I, six participants used a 6 6 matrix on 12 separate days with a mean rate of 1.2 selections/min and mean online and offline accuracies of 62% and 82%, respectively. In Phase II, four participants used either a 6 6o r a7 7 matrix to produce novel and spontaneous statements with a mean online rate of 2.1 selections/min and online accuracy of 79%. The amplitude and latency of the P300 remained stable over 40 weeks. Conclusions: Participants could communicate with the P300-based BCI and performance was stable over many months. Significance: BCIs could provide an alternative communication and control technology in the daily lives of people severely disabled by ALS.

Brain-computer interfaces (BCIs) translate brain activity into signals controlling external devices. BCIs based on visual stimuli can maintain communication in severely paralyzed patients, but only if intact vision is available. Debilitating neurological disorders however, may lead to loss of intact vision. The current study explores the feasibility of an auditory BCI. Sixteen healthy volunteers participated in three training sessions consisting of 30 2-3 min runs in which they learned to increase or decrease the amplitude of sensorimotor rhythms (SMR) of the EEG. Half of the participants were presented with visual and half with auditory feedback. Mood and motivation were assessed prior to each session. Although BCI performance in the visual feedback group was superior to the auditory feedback group there was no difference in performance at the end of the third session. Participants in the auditory feedback group learned slower, but four out of eight reached an accuracy of over 70% correct in the last session comparable to the visual feedback group. Decreasing performance of some participants in the visual feedback group is related to mood and motivation. We conclude that with sufficient training time an auditory BCI may be as efficient as a visual BCI. Mood and motivation play a role in learning to use a BCI.

The objective of this study was to quantify the number of segments that have contractile activity and determine the propagation speed from uterine electrophysiological signals recorded over the abdomen. The uterine magnetomyographic (MMG) signals were recorded with a 151 channel SARA (SQUID Array for Reproductive Assessment) system from 36 pregnant women between 37 and 40 weeks of gestational age. The MMG signals were scored and segments were classified based on presence of uterine contractile burst activity. The sensor space was then split into four quadrants and in each quadrant signal strength at each sample was calculated using center-of-gravity (COG). To this end, the cross-correlation analysis of the COG was performed to calculate the delay between pairwise combinations of quadrants. The relationship in propagation across the quadrants was quantified and propagation speeds were calculated from the delays. MMG recordings were successfully processed from 25 subjects and the average values of propagation speeds ranged from 1.3–9.5 cm/s, which was within the physiological range. The propagation was observed between both vertical and horizontal quadrants confirming multidirectional propagation. After the multiple pairwise test (99% CI), significant differences in speeds can be observed between certain vertical or horizontal combinations and the crossed pair combinations. The number of segments containing contractile activity in any given quadrant pair with a detectable delay was significantly higher in the lower abdominal pairwise combination as compared to all others. The quadrant-based approach using MMG signals provided us with high spatial-temporal information of the uterine contractile activity and will help us in the future to optimize abdominal electromyographic (EMG) recordings that are practical in a clinical setting.

In this pilot study near-infrared spectroscopy (NIRS) neurofeedback was investigated as a new method for the treatment of Attention Deficit-/Hyperactivity Disorder (ADHD). Oxygenated hemoglobin in the prefrontal cortex of children with ADHD was measured and fed back. 12 sessions of NIRS-neurofeedback were compared to the intermediate outcome after 12 sessions of EEG-neurofeedback (slow cortical potentials, SCP) and 12 sessions of EMG-feedback (muscular activity of left and right musculus supraspinatus). The task was either to increase or decrease hemodynamic activity in the prefrontal cortex (NIRS), to produce positive or negative shifts of SCP (EEG) or to increase or decrease muscular activity (EMG). In each group nine children with ADHD, aged 7–10 years, took part. Changes in parents’ ratings of ADHD symptoms were assessed before and after the 12 sessions and compared within and between groups. For the NIRS-group additional teachers’ ratings of ADHD symptoms, parents’ and teachers’ ratings of associated behavioral symptoms, childrens’ self reports on quality of life and a computer based attention task were conducted before, 4 weeks and 6 months after training. As primary outcome, ADHD symptoms decreased significantly 4 weeks and 6 months after the NIRS training, according to parents’ ratings. In teachers’ ratings of ADHD symptoms there was a significant reduction 4 weeks after the training. The performance in the computer based attention test improved significantly. Within-group comparisons after 12 sessions of NIRS-, EEG- and EMG-training revealed a significant reduction in ADHD symptoms in the NIRS-group and a trend for EEG- and EMG-groups. No significant differences for symptom reduction were found between the groups. Despite the limitations of small groups and the comparison of a completed with two uncompleted interventions, the results of this pilot study are promising. NIRS-neurofeedback could be a time-effective treatment for ADHD and an interesting new option to consider in the treatment of ADHD.

Amyotrophic lateral sclerosis (ALS) can result in the locked-in state (LIS), characterized by paralysis, and eventual respiratory failure, compensated by artificial ventilation,1 or the completely LIS (CLIS), with additional total paralysis of eye muscles. Brain–computer interfaces (BCIs) have been used to allow paralyzed people to regain basic communication,2 although current EEG-based BCIs have not succeeded with CLIS patients.3 We present Class IV case evidence to establish that communication in the CLIS is possible with a metabolic BCI based on near-infrared spectroscopy (NIRS).

Proceedings of the 6th International Brain-Computer Interface Conference 2014

In this pilot study near-infrared spectroscopy (NIRS) neurofeedback was investigated as a new method for the treatment of Attention Deficit-/Hyperactivity Disorder (ADHD). Oxygenated hemoglobin in the prefrontal cortex of children with ADHD was measured and fed back. 12 sessions of NIRS-neurofeedback were compared to the intermediate outcome after 12 sessions of EEG-neurofeedback (slow cortical potentials, SCP) and 12 sessions of EMG-feedback (muscular activity of left and right musculus supraspinatus). The task was either to increase or decrease hemodynamic activity in the prefrontal cortex (NIRS), to produce positive or negative shifts of SCP (EEG) or to increase or decrease muscular activity (EMG). In each group nine children with ADHD, aged 7-10 years, took part. Changes in parents' ratings of ADHD symptoms were assessed before and after the 12 sessions and compared within and between groups. For the NIRS-group additional teachers' ratings of ADHD symptoms, parents' and teachers' ratings of associated behavioral symptoms, childrens' self reports on quality of life and a computer based attention task were conducted before, 4 weeks and 6 months after training. As primary outcome, ADHD symptoms decreased significantly 4 weeks and 6 months after the NIRS training, according to parents' ratings. In teachers' ratings of ADHD symptoms there was a significant reduction 4 weeks after the training. The performance in the computer based attention test improved significantly. Within-group comparisons after 12 sessions of NIRS-, EEG- and EMG-training revealed a significant reduction in ADHD symptoms in the NIRS-group and a trend for EEG- and EMG-groups. No significant differences for symptom reduction were found between the groups. Despite the limitations of small groups and the comparison of a completed with two uncompleted interventions, the results of this pilot study are promising. NIRS-neurofeedback could be a time-effective treatment for ADHD and an interesting new option to consider in the treatment of ADHD. peerReviewed

We introduced a Pavlovian semantic conditioning paradigm to enable basic communication in completely locked-in state (CLIS). Patients in CLIS have no means of communication and they have represented the target population for brain–computer interface (BCI) research in the last 15 years. Although different paradigms have been tested as well as different physiological signals have been used, to date no documented CLIS patient was able to control a BCI over an extended time period. We designed a novel paradigm based on semantic conditioning for online classification of neuroelectric or any other physiological signals to discriminate between covert (cognitive) ‘yes’ and ‘no’ responses. The paradigm comprised the presentation of affirmative and negative statements used as conditioned stimuli and only affirmative statements were paired with electrical stimulation. A CLIS patient diagnosed with amyotrophic lateral sclerosis (ALS) participated in the study and underwent 37 daily sessions. The online classification accuracies of the slow cortical potentials, identified as the electroencephalographic (EEG) signature differentiating between covert ‘yes’ and ‘no’ responses, were around chance level on average, with peaks of high communication accuracy in some sessions.

Brain-computer interfaces (BCIs) provide a non-muscular communication channel for persons with severe motor impairments. Previous studies have shown that the aptitude with which a BCI can be controlled varies from person to person. A reliable predictor of performance could facilitate selection of a suitable BCI paradigm. Eleven severely motor impaired participants performed three sessions of a P300 BCI web browsing task. Before each session auditory oddball data were collected to predict the BCI aptitude of the participants exhibited in the current session. We found a strong relationship of early positive and negative potentials around 200 ms (elicited with the auditory oddball task) with performance. The amplitude of the P2 (r = −0.77) and of the N2 (r = −0.86) had the strongest correlations. Aptitude prediction using an auditory oddball was successful. The finding that the N2 amplitude is a stronger predictor of performance than P3 amplitude was reproduced after initially showing this effect with a healthy sample of BCI users. This will reduce strain on the end-users by minimizing the time needed to find suitable paradigms and inspire new approaches to improve performance.

We propose a classical semantic conditioning procedure to allow basic yes–no communication in the completely locked-in state as an alternative to instrumental-operant learning of brain responses, which is the common approach in brain–computer interface research. More precisely, it was intended to establish cortical responses to the trueness of a statement irrespective of the particular constituent words and letters or sounds of the words. As unconditioned stimulus short aversive stimuli consisting of 1-ms electrical pulses were used. True and false statements were presented acoustically and only the true statements were immediately followed by electrical stimuli. 15 healthy participants and one locked-in ALS patient underwent the experiment. Three different classifiers were employed in order to differentiate between the two cortical responses by means of electroencephalographic recordings. The offline analysis revealed that semantic classical conditioning can be applied successfully to enable basic communication using a non-muscular channel.

We propose a novel approach to calculate the conduction velocity (CV) of the uterine contraction bursts in magnetomyogram (MMG) signals measured using a multichannel SQUID array. For this purpose, we partition the sensor coordinates into four different quadrants and identify the contractile bursts using a previously proposed Hilbert-wavelet transform approach. If contractile burst is identified in more than one quadrant, we calculate the center of gravity (CoG) in each quadrant for each time point as the sum of the product of the sensor coordinates with the Hilbert amplitude of the MMG signals normalized by the sum of the Hilbert amplitude of the signals over all sensors. Following this we compute the delay between the CoGs of all (six) possible quadrant pairs combinations. As a first step, we validate this approach by simulating a stochastic model based on independent second-order autoregressive processes (AR2) and we divide them into 30 second disjoint windows and insert burst activity at specific time instances in preselected sensors. Also we introduce a lag of 5 ± 1 seconds between different quadrants. Using our approach we calculate the CoG of the signals in a quadrant. To this end, we compute the delay between CoGs obtained from different quadrants and show that our approach is able to reliably capture the delay incorporated in the model. We apply the proposed approach to 19 serial MMG data obtained from two subjects and show an increase in the CV as the subjects approached labor.

Premature labor and delivery is a major cause of infant mortality and morbidity. Advances in the fields of medicine and engineering together with a better understanding of the preterm birth related risk factors have successfully reduced their incidence. Despite such advances, in most developed countries the preterm birth rate is still high, accounting for about 85% of infant mortality and more than 50% of the surviving infant's morbidity. The timely prediction of premature labor and delivery can improve the effectiveness of the required treatments. Unfortunately, the techniques employed in current obstetrical practice proved to be inaccurate for the prediction of premature labor and delivery. The contractile element of the uterus is the myometrium, which consists of smooth muscle cells. The electrical activity (always accompanied by magnetic activity) in form of action potentials (AP) propagates through the myometrial cells causing the contraction of the uterus. Magnetomyography (MMG) is the noninvasive measurement of the uterine magnetic activity by means of equally spaced magnetic sensors arranged in a concave array. The MMG recordings display advantages which renders them suitable for the analysis and characterization of the uterine activity. Compared to electromyography signals, the MMG signals are detectable outside the boundary of the skin without making any contact with the body. Also they are independent on conductivity geometry, i.e. tissue conductivity. The measured electrical activity arises from volume currents owing (in the body) to the electrode sites and not directly due to the primary current generators. The current work presents a set of methods designated to identify and characterize uterine contractile activity and its dynamics in MMG signals. The basic process controlling the uterine contraction is the underlying electrical activity in the form of APs which propagate between muscle cells and open ion channels allowing the influx of calcium ions to produce a contraction. APs occur in groups and form a burst of activity which in humans can last more than a minute. The ultimate goal is to provide a timely prediction of delivery. More precisely, a multi-sensor analysis of the spatial propagation properties of the MMG signals is carried out to identify time segments of uterine burst activity. This type of analysis is of particular relevance because the spreading of magnetic activity in the myometrium results in coordinated contractions (close to term), capable to push the fetus into the birth canal and ultimately lead to delivery. Therefore, the analysis of the spatial propagation properties (within segments of contractile activity), i.e. conduction velocity (CV), could provide a fundamental contribution for the prediction of delivery. It could be shown that the HTWD approach can be used for the successful identification of uterine contractions. To mark the contractile intervals, a discrete-time binary decision signal was created in each magnetic sensor. The information provided by this approach was further used to determine the CV of the uterine contraction bursts and it was shown that the increase in the CV can be considered as a possible predictor of preterm labor. Significant benefits could be expected from the introduction of MMG signal analysis for routine contraction monitoring. Further clinical validations are required to assess the robustness of the presented methods and to account for physiological differences among subjects. Frühgeburt ist die Hauptursache für Säuglingssterblichkeit und Säuglingsmorbidität. Die Inzidenz der Frühgeburt konnte durch Fortschritte in der biomedizinischen Forschung und ein besseres Verständnis der Risikofaktoren erfolgreich reduziert werden. Trotz solcher Erfolge, ist die Häufigkeit der Frühgeburt auch in entwickelten Ländern noch hoch und erklärt etwa 85% der Säuglingssterblichkeit und mehr als 50% der überlebenden Säuglingsmorbidität. Eine zeitliche Vorhersage des Eintretens einer Frühgeburt kann die Effektivität der benötigten Behandlungen erhöhen, jedoch sind die gegenwärtig eingesetzten Methoden für die Vorhersage der Frühgeburt zu ungenau. Das kontraktile Gewebe der Gebärmutter ist das Myometrium, das aus einer glatten Muskulatur besteht. Die elektrische Aktivität (immer begleitet von einer magnetischen Aktivität) in Form von Aktionspotentialen (AP) breitet sich entlang der myometrischen Zelloberäche aus und verursacht Kontraktionen des Uterus. Die Magnetomyographie (MMG) ist eine nicht invasive Methode für die Messung der magnetischen Aktivität des Uterus. Dabei werden hochsensitive magnetische Sensoren eingesetzt, die es erlauben magnetische Aktivität über das gesamte Abdomen einer Schwangeren zu registrieren. Aus den biomagnetischen Signalen kann die kontraktile Aktivität des Uterus extrahiert werden. Im Vergleich zu elektromyographischen Signalen können MMG Signale auch ohne direkten Kontakt mit der Hautoberäche erfasst werden. Zusätzlich werden die MMG Signale nicht von der Leitfähigkeit des Gewebes beeinusst und können auch referenzfrei registriert werden. In der vorliegenden Arbeit wurden mehrere Methoden entwickelt, implementiert und validiert, die für die Identifizierung und Charakterisierung der kontraktilen Aktivität des Uterus und seiner Dynamik geeignet sind. Das Hauptziel der Arbeit war die Entwicklung eines Methodensets, das die zeitliche Vorhersage der Geburt ermöglichen kann. Dabei wurde die kontraktile Aktivität anhand einer Multi-Sensor Analyse der räumlich-zeitlichen Muster der MMG Signale untersucht. Die Ausbreitung der magnetischen Aktivität im Myometrium verursacht koordinierte, die den Fötus in den Geburtskanal drücken und schließlich zur Geburt führen. Es wurde gezeigt, dass der HTWD Ansatz für eine erfolgreiche Identifizierung der Wehentätigkeit im MMG Signal verwendet werden kann. Um die kontraktilen Abständen zu markieren, wurde ein zeitdiskretes, binäres Auswahlsignal in jedem magnetischen Sensor erzeugt. Die von diesem Ansatz zur Verfügung gestellten Informationen wurden weiter verwendet, um die CV der kontraktilen Aktivität im Uterus zu bestimmen. Es wurde auch gezeigt, dass die CV als möglicher Prädiktor für vorzeitige Wehen betrachtet werden kann. Der Einsatz der MMG Signalanalyse für die routinemäßige überwachung der Wehentätigkeit im klinischen Setting könnte wichtige medizinische Nutzen zeigen. Weitere klinische Validierungen, die die physiologische Unterschiede zwischen Probanden berücksichtigen, sind erforderlich, um die Robustheit der vorgestellten Methoden zu bewerten.

The goal of the current study is to find a suitable classifier for electroencephalogram (EEG) data derived from a new learning paradigm which aims at communication in paralysis. A reflexive semantic classical (Pavlovian) conditioning paradigm is explored as an alternative to the operant learning paradigms, currently used in most brain–computer interfaces (BCIs). Comparable with a lie-detection experiment, subjects are presented with true and false statements. The EEG activity following true and false statements was classified with the aim to separate covert ‘yes’ from covert ‘no’ responses. Four classification algorithms are compared for classifying off-line data collected from a group of 14 healthy participants: (i) stepwise linear discriminant analysis (SWLDA), (ii) shrinkage linear discriminant analysis (SLDA), (iii) linear support vector machine (LIN-SVM) and (iv) radial basis function kernel support vector machine (RBF-SVM). The results indicate that all classifiers perform at chance level when separating conditioned ‘yes’ from conditioned ‘no’ responses. However, single conditioned reactions could be successfully classified on a single-trial basis (single conditioned reaction against a baseline interval). All of the four investigated classification methods achieve comparable performance, however results with RBF-SVM show the highest single-trial classification accuracy of 68.8%. The results suggest that the proposed paradigm may allow affirmative and negative (disapproving negative) communication in a BCI experiment.

We propose a novel approach based on Hilbert phase to identify the burst in the uterine myometrial activity. We apply this approach to 24 serial magnetomyographic signals recorded from four pregnant women using a 151 SQUID array system. The bursts identified with this approach are evaluated for duration and are correlated with the gestational age. In all four subjects, we find a decrease in the duration of burst as the subject approaches active labor. As was shown in animal studies, this result indicates a faster conduction time between the muscle cells which activate a larger number of muscle units in a synchronous manner.

We propose a multi-stage approach using Wavelet and Hilbert transforms to identify uterine contraction bursts in magnetomyogram (MMG) signals measured using a 151 magnetic sensor array. In the first stage, we decompose the MMG signals by wavelet analysis into multilevel approximate and detail coefficients. In each level, the signals are reconstructed using the detail coefficients followed by the computation of the Hilbert transform. The Hilbert amplitude of the reconstructed signals from different frequency bands (0.1–1 Hz) is summed up over all the sensors to increase the signal-to-noise ratio. Using a novel clustering technique, affinity propagation, the contractile bursts are distinguished from the noise level. The method is applied on simulated MMG data, using a simple stochastic model to determine its robustness and to seven MMG datasets.

This study was designed to develop and test an auditory event-related potential (ERP) based spelling system for a brain-computer interface (BCI) and to compare user's performance between the auditory and visual modality. The spelling system, where letters in a matrix were coded with acoustically presented numbers, was tested on a group of healthy volunteers. The results were compared with a visual spelling system. Nine of the 13 participants presented with the auditory ERP spelling system scored above a predefined criterion level control for communication. Compared to the visual spelling system, users' performance was lower and the peak latencies of the auditorily evoked ERPs were delayed. It was concluded that auditorily evoked ERPs from the majority of the users could be reliably classified. High accuracies were achieved in these users, rendering item selection with a BCI based on auditory stimulation feasible for communication.

An overview of different approaches to brain-computer interfaces (BCIs) developed in our laboratory is given. An important clinical application of BCIs is to enable communication or environmental control in severely paralyzed patients. The BCI “Thought-Translation Device (TTD)” allows verbal communication through the voluntary self-regulation of brain signals (e.g., slow cortical potentials (SCPs)), which is achieved by operant feedback training. Humans' ability to self-regulate their SCPs is used to move a cursor toward a target that contains a selectable letter set. Two different approaches were followed to developWeb browsers that could be controlled with binary brain responses. Implementing more powerful classification methods including different signal parameters such as oscillatory features improved our BCI considerably. It was also tested on signals with implanted electrodes. Most BCIs provide the user with a visual feedback interface. Visually impaired patients require an auditory feedback mode. A procedure using auditory (sonified) feedback of multiple EEG parameters was evaluated. Properties of the auditory systems are reported and the results of two experiments with auditory feedback are presented. Clinical data of eight ALS patients demonstrated that all patients were able to acquire efficient brain control of one of the three available BCI systems (SCP, µ-rhythm, and P300), most of them used the SCP-BCI. A controlled comparison of the three systems in a group of ALS patients, however, showed that P300-BCI and the µ-BCI are faster and more easily acquired than SCP-BCI, at least in patients with some rudimentary motor control left. Six patients who started BCI training after entering the completely locked-in state did not achieve reliable communication skills with any BCI system. One completely locked-in patient was able to communicate shortly with a ph-meter, but lost control afterward.