Empirical Comparison of Distributed Source Localization Methods for Single-Trial Detection of Movement Preparation.

The development of technologies for the treatment of movement disorders, like stroke, is still of particular interest in brain-computer interface (BCI) research. In this context, source localization methods (SLMs), that reconstruct the cerebral origin of brain activity measured outside the head, e.g., via electroencephalography (EEG), can add a valuable insight into the current state and progress of the treatment. However, in BCIs SLMs were often solely considered as advanced signal processing methods that are compared against other methods based on the classification performance alone. Though, this approach does not guarantee physiological meaningful results. We present an empirical comparison of three established distributed SLMs with the aim to use one for single-trial movement prediction. The SLMs wMNE, sLORETA, and dSPM were applied on data acquired from eight subjects performing voluntary arm movements. Besides the classification performance as quality measure, a distance metric was used to asses the physiological plausibility of the methods. For the distance metric, which is usually measured to the source position of maximum activity, we further propose a variant based on clusters that is better suited for the single-trial case in which several sources are likely and the actual maximum is unknown. The two metrics showed different results. The classification performance revealed no significant differences across subjects, indicating that all three methods are equally well-suited for single-trial movement prediction. On the other hand, we obtained significant differences in the distance measure, favoring wMNE even after correcting the distance with the number of reconstructed clusters. Further, distance results were inconsistent with the traditional method using the maximum, indicating that for wMNE the point of maximum source activity often did not coincide with the nearest activation cluster. In summary, the presented comparison might help users to select an appropriate SLM and to understand the implications of the selection. The proposed methodology pays attention to the particular properties of distributed SLMs and can serve as a framework for further comparisons.

An Adaptive Spatial Filter for User-Independent Single Trial Detection of Event-Related Potentials.

GOAL: Current brain-computer interfaces (BCIs) are usually based on various, often supervised, signal processing methods. The disadvantage of supervised methods is the requirement to calibrate them with recently acquired subject-specific training data. Here, we present a novel algorithm for dimensionality reduction (spatial filter), that is ideally suited for single-trial detection of event-related potentials (ERPs) and can be adapted online to a new subject to minimize or avoid calibration time. METHODS: The algorithm is based on the well-known xDAWN filter, but uses generalized eigendecomposition to allow an incremental training by recursive least squares (RLS) updates of the filter coefficients. We analyze the effectiveness of the spatial filter in different transfer scenarios and combinations with adaptive classifiers. RESULTS: The results show that it can compensate changes due to switching between different users, and therefore allows to reuse training data that has been previously recorded from other subjects. CONCLUSIONS: The presented approach allows to reduce or completely avoid a calibration phase and to instantly use the BCI system with only a minor decrease of performance. SIGNIFICANCE: The novel filter can adapt a precomputed spatial filter to a new subject and make a BCI system user independent.

How to evaluate an agent's behavior to infrequent events?-Reliable performance estimation insensitive to class distribution.

In everyday life, humans and animals often have to base decisions on infrequent relevant stimuli with respect to frequent irrelevant ones. When research in neuroscience mimics this situation, the effect of this imbalance in stimulus classes on performance evaluation has to be considered. This is most obvious for the often used overall accuracy, because the proportion of correct responses is governed by the more frequent class. This imbalance problem has been widely debated across disciplines and out of the discussed treatments this review focusses on performance estimation. For this, a more universal view is taken: an agent performing a classification task. Commonly used performance measures are characterized when used with imbalanced classes. Metrics like Accuracy, F-Measure, Matthews Correlation Coefficient, and Mutual Information are affected by imbalance, while other metrics do not have this drawback, like AUC, d-prime, Balanced Accuracy, Weighted Accuracy and G-Mean. It is pointed out that one is not restricted to this group of metrics, but the sensitivity to the class ratio has to be kept in mind for a proper choice. Selecting an appropriate metric is critical to avoid drawing misled conclusions.

On the applicability of brain reading for predictive human-machine interfaces in robotics.

The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors.

Human force discrimination during active arm motion for force feedback design.

The goal of this study was to analyze the human ability of external force discrimination while actively moving the arm. With the approach presented here, we give an overview for the whole arm of the just-noticeable differences (JNDs) for controlled movements separately executed for the wrist, elbow, and shoulder joints. The work was originally motivated in the design phase of the actuation system of a wearable exoskeleton, which is used in a teleoperation scenario where force feedback should be provided to the subject. The amount of this force feedback has to be calibrated according to the human force discrimination abilities. In the experiments presented here, 10 subjects performed a series of movements facing an opposing force from a commercial haptic interface. Force changes had to be detected in a two-alternative forced choice task. For each of the three joints tested, perceptual thresholds were measured as absolute thresholds (no reference force) and three JNDs corresponding to three reference forces chosen. For this, we used the outcome of the QUEST procedure after 70 trials. Using these four measurements we computed the Weber fraction. Our results demonstrate that different Weber fractions can be measured with respect to the joint. These were 0.11, 0.13, and 0.08 for wrist, elbow, and shoulder, respectively. It is discussed that force perception may be affected by the number of muscles involved and the reproducibility of the movement itself. The minimum perceivable force, on average, was 0.04 N for all three joints.

pySPACE-a signal processing and classification environment in Python.

In neuroscience large amounts of data are recorded to provide insights into cerebral information processing and function. The successful extraction of the relevant signals becomes more and more challenging due to increasing complexities in acquisition techniques and questions addressed. Here, automated signal processing and machine learning tools can help to process the data, e.g., to separate signal and noise. With the presented software pySPACE (http://pyspace.github.io/pyspace), signal processing algorithms can be compared and applied automatically on time series data, either with the aim of finding a suitable preprocessing, or of training supervised algorithms to classify the data. pySPACE originally has been built to process multi-sensor windowed time series data, like event-related potentials from the electroencephalogram (EEG). The software provides automated data handling, distributed processing, modular build-up of signal processing chains and tools for visualization and performance evaluation. Included in the software are various algorithms like temporal and spatial filters, feature generation and selection, classification algorithms, and evaluation schemes. Further, interfaces to other signal processing tools are provided and, since pySPACE is a modular framework, it can be extended with new algorithms according to individual needs. In the presented work, the structural hierarchies are described. It is illustrated how users and developers can interface the software and execute offline and online modes. Configuration of pySPACE is realized with the YAML format, so that programming skills are not mandatory for usage. The concept of pySPACE is to have one comprehensive tool that can be used to perform complete signal processing and classification tasks. It further allows to define own algorithms, or to integrate and use already existing libraries.

Visual detection and identification are not the same: evidence from psychophysics and fMRI.

Sometimes object detection as opposed to identification is sufficient to initiate the appropriate action. To explore the neural origin of behavioural differences between the two tasks, we combine psychophysical measurements and fMRI, specifically contrasting shape detection versus identification of a figure. This figure consisted of Gabor elements being oriented differently from those in the background. We equalized performance levels for detection and identification by adjusting orientation differences accordingly for each observer. Hence, stimulus saliency was constant for both tasks allowing a differentiation between the activations specific for detection versus identification processes. Identification yielded higher psychophysical thresholds, slower reaction times and increased hemodynamic activations in the lateral-occipital complex (LOC) and an adjacent area in the collateral sulcus (CoS). Additional analysis using cortex-based alignment revealed four voxel-clusters differentially activated by the tasks, situated in the inferior parietal lobe, the precuneus, the anterior cingulum and the medial frontal gyrus. Our results indicate partly separated cortical mechanisms for object detection and identification.

Electrophysiological correlates of figure-ground segregation directly reflect perceptual saliency.

In a figure identification task, we investigated the influence of different visual cue configurations (spatial frequency, orientation or a combination of both) on the human EEG. Combining psychophysics with ERP and time-frequency analysis, we show that the neural response at about 200ms reflects perceptual saliency rather than physical cue contrast. Increasing saliency caused (i) a negative shift of the posterior P2 coinciding with a power decrease in the posterior theta-band and (ii) an amplitude and latency increase of the posterior P3. We demonstrate that visual cues interact for a percept that is non-linearly related to the physical figure-ground properties.

The electrophysiological correlate of saliency: evidence from a figure-detection task.

Although figure-ground segregation in a natural environment usually relies on multiple cues, we experience a coherent figure without usually noticing the individual single cues. It is still unclear how various cues interact to achieve this unified percept and whether this interaction depends on task demands. Studies investigating the effect of cue combination on the human EEG are still lacking. In the present study, we combined psychophysics, ERP and time-frequency analysis to investigate the interaction of orientation and spatial frequency as visual cues in a figure detection task. The figure was embedded in a matrix of Gabor elements, and we systematically varied figure saliency by changing the underlying cue configuration. We found a strong correlation between the posterior P2 amplitude and the perceived saliency of the figure: the P2 amplitude decreased with increasing saliency. Analogously, the power of the theta-band decreased for more salient figures. At longer latencies, the posterior P3 component was modulated in amplitude and latency, possibly reflecting increased decision confidence at higher saliencies. In conclusion, when the cue composition (e.g. one or two cues) or cue strength is changed in a figure detection task, first differences in the electrophysiological response reflect the perceived saliency and not directly the underlying cue configuration.

Exact subthreshold integration with continuous spike times in discrete-time neural network simulations.

Very large networks of spiking neurons can be simulated efficiently in parallel under the constraint that spike times are bound to an equidistant time grid. Within this scheme, the subthreshold dynamics of a wide class of integrate-and-fire-type neuron models can be integrated exactly from one grid point to the next. However, the loss in accuracy caused by restricting spike times to the grid can have undesirable consequences, which has led to interest in interpolating spike times between the grid points to retrieve an adequate representation of network dynamics. We demonstrate that the exact integration scheme can be combined naturally with off-grid spike events found by interpolation. We show that by exploiting the existence of a minimal synaptic propagation delay, the need for a central event queue is removed, so that the precision of event-driven simulation on the level of single neurons is combined with the efficiency of time-driven global scheduling. Further, for neuron models with linear subthreshold dynamics, even local event queuing can be avoided, resulting in much greater efficiency on the single-neuron level. These ideas are exemplified by two implementations of a widely used neuron model. We present a measure for the efficiency of network simulations in terms of their integration error and show that for a wide range of input spike rates, the novel techniques we present are both more accurate and faster than standard techniques.

Pattern-onset stimulation boosts central multifocal VEP responses.

Multifocal visual evoked potentials (VEP) allow one to assess whether stimulation at specific visual field locations elicits cortical activity; it might therefore enable us to conduct objective visual field perimetry. However, due to the cortical folding, which differs markedly between subjects, a particular electroencephalogram generator may fail to project signal on some recording electrodes. This may lead to false alarms for potential scotomata. Here we compare pattern-reversal and pattern-onset stimulation in their efficacy to activate the visual cortex and recorded mfVEPs to 60 locations comprising a visual field of 44 degrees diameter. We report three main findings: (1) Pattern-onset compared to pattern-reversal enhances the amplitude by 30% for stimulation of the central visual field (<10 degrees radius), while evoking 30% less response in the periphery (>15 degrees ). (2) Although pattern-onset and pattern-reversal responses differ markedly in their eccentricity dependence, they have a similar topographical distribution. (3) By combining both stimuli, the number of false positives was reduced to less than 1.5% of the visual field locations tested. We conclude that pattern-onset and pattern-reversal activate identical visual cortical areas but target different neural mechanisms within these areas. Furthermore, pattern-onset stimulation greatly increases the sensitivity of the mfVEP to assess the cortical representation of the central 10 degrees of the visual field.