Age-related differences in finger interdependence during complex hand movements.

The well-known decrease in finger dexterity during healthy aging leads to a significant reduction in quality of life. Still, the exact patterns of altered finger kinematics of older adults in daily life are fairly unexplored. Finger interdependence is the unintentional co-movement of fingers that are not intended to move, and it is known to vary across the lifespan. Nevertheless, the magnitude and direction of age-related differences in finger interdependence are ambiguous across studies and tasks and have not been explored in the context of daily life finger movements. We investigated five different free and daily-life-inspired finger movements of the right, dominant hand as well as a sequential finger tapping task of the thumb against the other fingers, in 17 younger (22 to 37 years) and 17 older (62 to 80 years) adults using an exoskeleton data glove for data recording. Using inferential statistics, we found that the unintentional co-movement of fingers generally decreases with age in all performed daily-life-inspired movements. Finger tapping, however, showed a trend towards higher finger interdependence for older compared to younger adults. Using machine learning, we predicted the age group of a person from finger interdependence features of single movement trials significantly better than chance level for the daily-life-inspired movements, but not for finger tapping. Taken together, we show that for specific tasks, decreased finger interdependence (i.e., less co-movement) could potentially act as a marker of human aging that specifically characterizes older adults' complex finger movements in daily life.

A toolbox for decoding BCI commands based on event-related potentials.

Commands in brain-computer interface (BCI) applications often rely on the decoding of event-related potentials (ERP). For instance, the P300 potential is frequently used as a marker of attention to an oddball event. Error-related potentials and the N2pc signal are further examples of ERPs used for BCI control. One challenge in decoding brain activity from the electroencephalogram (EEG) is the selection of the most suitable channels and appropriate features for a particular classification approach. Here we introduce a toolbox that enables ERP-based decoding using the full set of channels, while automatically extracting informative components from relevant channels. The strength of our approach is that it handles sequences of stimuli that encode multiple items using binary classification, such as target vs. nontarget events typically used in ERP-based spellers. We demonstrate examples of application scenarios and evaluate the performance of four openly available datasets: a P300-based matrix speller, a P300-based rapid serial visual presentation (RSVP) speller, a binary BCI based on the N2pc, and a dataset capturing error potentials. We show that our approach achieves performances comparable to those in the original papers, with the advantage that only conventional preprocessing is required by the user, while channel weighting and decoding algorithms are internally performed. Thus, we provide a tool to reliably decode ERPs for BCI use with minimal programming requirements.

Reduced dimension stimulus decoding and column-based modeling reveal architectural differences of primary somatosensory finger maps between younger and older adults.

The primary somatosensory cortex (SI) contains fine-grained tactile representations of the body, arranged in an orderly fashion. The use of ultra-high resolution fMRI data to detect group differences, for example between younger and older adults' SI maps, is challenging, because group alignment often does not preserve the high spatial detail of the data. Here, we use robust-shared response modeling (rSRM) that allows group analyses by mapping individual stimulus-driven responses to a lower dimensional shared feature space, to detect age-related differences in tactile representations between younger and older adults using 7T-fMRI data. Using this method, we show that finger representations are more precise in Brodmann-Area (BA) 3b and BA1 compared to BA2 and motor areas, and that this hierarchical processing is preserved across age groups. By combining rSRM with column-based decoding (C-SRM), we further show that the number of columns that optimally describes finger maps in SI is higher in younger compared to older adults in BA1, indicating a greater columnar size in older adults' SI. Taken together, we conclude that rSRM is suitable for finding fine-grained group differences in ultra-high resolution fMRI data, and we provide first evidence that the columnar architecture in SI changes with increasing age.

Parietal cortical alpha/beta suppression during prospective memory retrieval.

Prospective memory (PM) impairment is among the most frequent memory complaints, yet little is known about the underlying neural mechanisms. PM for a planned intention may be achieved through strategic monitoring of the environment for cues, involving ongoing attentional processes, or through spontaneous retrieval. We hypothesized that parietal spectral power modulation accompanies prospectively encoded intention retrieval, irrespective of PM retrieval approach. A cognitively engaging arithmetic-based ongoing task (OGT) was employed to encourage spontaneous retrieval, with a focal, internally generated PM cue to eliminate OGT/PM trial differentiation based on perceptual or conceptual PM cue features. Two PM repetition frequencies were used to vary the extent of strategic monitoring. We observed a transient parietal alpha/beta spectral power reduction directly preceding the response, which was distinguishable on a single trial basis, as revealed by an OGT/PM trial classification rate exceeding 70% using linear discriminant analysis. The alpha/beta idling rhythm reflects cortical inhibition. A disengagement of task-relevant neural assemblies from this rhythm, reflected in alpha/beta power reduction, is deemed to increase information content, facilitate information integration, and enable engagement of neural assemblies in task-related cortical networks. The observed power reduction is consistent with the Dual Pathways model, where PM strategies converge at the PM retrieval stage.

Repetitive Anodal TDCS to the Frontal Cortex Increases the P300 during Working Memory Processing.

Transcranial direct current stimulation (TDCS) is a technique with which neuronal activity, and therefore potentially behavior, is modulated by applying weak electrical currents to the scalp. Application of TDCS to enhance working memory (WM) has shown promising but also contradictory results, and little emphasis has been placed on repeated stimulation protocols, in which effects are expected to be increased. We aimed to characterize potential behavioral and electrophysiological changes induced by TDCS during WM training and evaluate whether repetitive anodal TDCS has a greater modulatory impact on the processes underpinning WM than single-session stimulation. We examined the effects of single-session and repetitive anodal TDCS to the dorsolateral prefrontal cortex (DLPFC), targeting the frontal-parietal network, during a WM task in 20 healthy participants. TDCS had no significant impact on behavioral measures, including reaction time and accuracy. Analyzing the electrophysiological response, the P300 amplitude significantly increased following repetitive anodal TDCS, however, positively correlating with task performance. P300 changes were identified over the parietal cortex, which is known to engage with the frontal cortex during WM processing. These findings support the hypothesis that repetitive anodal TDCS modulates electrophysiological processes underlying WM.

Distinct interacting cortical networks for stimulus-response and repetition-suppression.

Non-invasive studies consider the initial neural stimulus response (SR) and repetition suppression (RS) - the decreased response to repeated sensory stimuli - as engaging the same neurons. That is, RS is a suppression of the SR. We challenge this conjecture using electrocorticographic (ECoG) recordings with high spatial resolution in ten patients listening to task-irrelevant trains of auditory stimuli. SR and RS were indexed by high-frequency activity (HFA) across temporal, parietal, and frontal cortices. HFASR and HFARS were temporally and spatially distinct, with HFARS emerging later than HFASR and showing only a limited spatial intersection with HFASR: most HFASR sites did not demonstrate HFARS, and HFARS was found where no HFASR could be recorded. ß activity was enhanced in HFARS compared to HFASR cortical sites. θ activity was enhanced in HFASR compared to HFARS sites. Furthermore, HFASR sites propagated information to HFARS sites via transient θ:ß phase-phase coupling. In contrast to predictive coding (PC) accounts our results indicate that HFASR and HFARS are functionally linked but have minimal spatial overlap. HFASR might enable stable and rapid perception of environmental stimuli across extended temporal intervals. In contrast HFARS might support efficient generation of an internal model based on stimulus history.

Discriminating Free Hand Movements Using Support Vector Machine and Recurrent Neural Network Algorithms.

Decoding natural hand movements is of interest for human-computer interaction and may constitute a helpful tool in the diagnosis of motor diseases and rehabilitation monitoring. However, the accurate measurement of complex hand movements and the decoding of dynamic movement data remains challenging. Here, we introduce two algorithms, one based on support vector machine (SVM) classification combined with dynamic time warping, and the other based on a long short-term memory (LSTM) neural network, which were designed to discriminate small differences in defined sequences of hand movements. We recorded hand movement data from 17 younger and 17 older adults using an exoskeletal data glove while they were performing six different movement tasks. Accuracy rates in decoding the different movement types were similarly high for SVM and LSTM in across-subject classification, but, for within-subject classification, SVM outperformed LSTM. The SVM-based approach, therefore, appears particularly promising for the development of movement decoding tools, in particular if the goal is to generalize across age groups, for example for detecting specific motor disorders or tracking their progress over time.

Numerical and biomechanical analysis of orthodontic treatment of recovered periodontally compromised patients.

OBJECTIVE: Generate a finite element (FE) model to simulate space closure and retraction mechanics for anterior maxillary teeth in periodontally compromised dentition, and compare the biomechanical effect of initial force systems with varying magnitude. MATERIALS AND METHODS: The geometry of an idealized finite element model (FEM) of a maxilla was adapted such that the teeth showed reduced periodontal support together with extruded and flared incisors. In a first step, leveling and alignment of the front teeth were simulated. In a second step, force systems for orthodontic space closure of residual spaces on both sides distal to the lateral incisors were simulated. A combined intrusion and retraction cantilever was modeled, to simulate en masse retraction mechanics with segmented arches and elastic chains. A commercial FE system was used for all model generations and simulations. RESULTS: Results of the simulations indicated that a force of 1.0 N is too high for space closure of flared front teeth in periodontally damaged dentition, as extreme strains may occur. En masse retraction using cantilever mechanics with lower forces showed a uniform intrusion and retraction movement and thus proved to be a better option for treating patients with a periodontally compromised dentition. CONCLUSION: The outcome of this study indicates that increased periodontal stresses resulting from severe attachment loss should be seriously considered by careful planning of the orthodontic mechanics and reduction of the applied forces is suggested. The presented cantilever mechanics seems to be an appropriate means for en masse retraction of periodontally compromised extruded front teeth.

Mind-wandering Is Accompanied by Both Local Sleep and Enhanced Processes of Spatial Attention Allocation.

Mind-wandering (MW) is a subjective, cognitive phenomenon, in which thoughts move away from the task toward an internal train of thoughts, possibly during phases of neuronal sleep-like activity (local sleep, LS). MW decreases cortical processing of external stimuli and is assumed to decouple attention from the external world. Here, we directly tested how indicators of LS, cortical processing, and attentional selection change in a pop-out visual search task during phases of MW. Participants' brain activity was recorded using magnetoencephalography, MW was assessed via self-report using randomly interspersed probes. As expected, the performance decreased under MW. Consistent with the occurrence of LS, MW was accompanied by a decrease in high-frequency activity (HFA, 80-150 Hz) and an increase in slow wave activity (SWA, 1-6 Hz). In contrast, visual attentional selection as indexed by the N2pc component was enhanced during MW with the N2pc amplitude being directly linked to participants' performance. This observation clearly contradicts accounts of attentional decoupling that would predict a decrease in attention-related responses to external stimuli during MW. Together, our results suggest that MW occurs during phases of LS with processes of attentional target selection being upregulated, potentially to compensate for the mental distraction during MW.

Impact of Stimulus Features on the Performance of a Gaze-Independent Brain-Computer Interface Based on Covert Spatial Attention Shifts.

Regaining communication abilities in patients who are unable to speak or move is one of the main goals in decoding brain waves for brain-computer interface (BCI) control. Many BCI approaches designed for communication rely on attention to visual stimuli, commonly applying an oddball paradigm, and require both eye movements and adequate visual acuity. These abilities may, however, be absent in patients who depend on BCI communication. We have therefore developed a response-based communication BCI, which is independent of gaze shifts but utilizes covert shifts of attention to the left or right visual field. We recorded the electroencephalogram (EEG) from 29 channels and coregistered the vertical and horizontal electrooculogram. Data-driven decoding of small attention-based differences between the hemispheres, also known as N2pc, was performed using 14 posterior channels, which are expected to reflect correlates of visual spatial attention. Eighteen healthy participants responded to 120 statements by covertly directing attention to one of two colored symbols (green and red crosses for "yes" and "no," respectively), presented in the user's left and right visual field, respectively, while maintaining central gaze fixation. On average across participants, 88.5% (std: 7.8%) of responses were correctly decoded online. In order to investigate the potential influence of stimulus features on accuracy, we presented the symbols with different visual angles, by altering symbol size and eccentricity. The offline analysis revealed that stimulus features have a minimal impact on the controllability of the BCI. Hence, we show with our novel approach that spatial attention to a colored symbol is a robust method with which to control a BCI, which has the potential to support severely paralyzed people with impaired eye movements and low visual acuity in communicating with their environment.

Early Shift of Attention Is Not Regulated by Mind Wandering in Visual Search.

Unique to humans is the ability to report subjective awareness of a broad repertoire of external and internal events. Even when asked to focus on external information, the human's mind repeatedly wanders to task-unrelated thoughts, which limits reading comprehension or the ability to withhold automated manual responses. This led to the attentional decoupling account of mind wandering (MW). However, manual responses are not an ideal parameter to study attentional decoupling, given that during MW, the online adjustment of manual motor responses is impaired. Hence, whether early attentional mechanisms are indeed downregulated during MW or only motor responses being slowed is not clear. In contrast to manual motor responses, eye movements are considered a sensitive proxy of attentional shifts. Using a simple target detection task, we asked subjects to indicate whether a target was presented within a visual search display by pressing a button while we recorded eye movements and unpredictably asked the subjects to rate their actual level of MW. Generally, manual reaction times increased with MW, both in target absent and present trials. But importantly, even in trials with MW, subjects detected earlier a presented than an absent target. The decoupling account would predict more fixations of the target before pressing the button during MW. However, our results did not corroborate this assumption. Most importantly, subject's time to direct gaze at the target was equally fast in trials with and without MW. Our results corroborate our hypothesis that during MW early, bottom-up driven attentional processes are not decoupled but selectively manual motor responses are slowed.

Decoding the covert shift of spatial attention from electroencephalographic signals permits reliable control of a brain-computer interface.

OBJECTIVE: One of the main goals of brain-computer interfaces (BCI) is to restore communication abilities in patients. BCIs often use event-related potentials (ERPs) like the P300 which signals the presence of a target in a stream of stimuli. The P300 and related approaches, however, are inherently limited, as they require many stimulus presentations to obtain a usable control signal. Many approaches depend on gaze direction to focus the target, which is also not a viable approach in many cases, because eye movements might be impaired in potential users. Here we report on a BCI that avoids both shortcomings by decoding spatial target information, independent of gaze shifts. APPROACH: We present a new method to decode from the electroencephalogram (EEG) covert shifts of attention to one out of four targets simultaneously presented in the left and right visual field. The task is designed to evoke the N2pc component-a hemisphere lateralized response, elicited over the occipital scalp contralateral to the attended target. The decoding approach involves decoding of the N2pc based on data-driven estimation of spatial filters and a correlation measure. MAIN RESULTS: Despite variability of decoding performance across subjects, 22 out of 24 subjects performed well above chance level. Six subjects even exceeded 80% (cross-validated: 89%) correct predictions in a four-class discrimination task. Hence, the single-trial N2pc proves to be a component that allows for reliable BCI control. An offline analysis of the EEG data with respect to their dependence on stimulation time and number of classes demonstrates that the present method is also a workable approach for two-class tasks. SIGNIFICANCE: Our method extends the range of strategies for gaze-independent BCI control. The proposed decoding approach has the potential to be efficient in similar applications intended to decode ERPs.

Self-regulated critical brain dynamics originate from high frequency-band activity in the MEG.

Brain function requires the flexible coordination of billions of neurons across multiple scales. This could be achieved by scale-free, critical dynamics balanced at the edge of order and disorder. Criticality has been demonstrated in several, often reduced neurophysiological model systems. In the intact human brain criticality has yet been only verified for the resting state. A more direct link between the concept of criticality and oscillatory brain physiology, which is strongly related to cognition, is yet missing. In the present study we therefore carried out a frequency-specific analysis of criticality in the MEG, recorded while subjects were in a defined cognitive state through mindfulness meditation. In a two-step approach we assessed whether the macroscopic neural avalanche dynamics is scale-free by evaluating the goodness of a power-law fits of cascade size and duration distributions of MEG deflections in different frequency bands. In a second step we determined the closeness of the power-law exponents to a critical value of -1.5. Power-law fitting was evaluated by permutation testing, fitting of alternative distributions, and cascade shape analysis. Criticality was verified by defined relationships of exponents of cascade size and duration distributions. Behavioral relevance of criticality was tested by correlation of indices of criticality with individual scores of the Mindful Attention Awareness Scale. We found that relevant scale-free near-critical dynamics originated only from broad-band high-frequency (> 100 Hz) MEG activity, which has been associated with action potential firing, and therefore links criticality on the macroscopic level of MEG to critical spike avalanches on a microscopic level. Whereas a scale-free dynamics was found under mindfulness meditation and rest, avalanche dynamics shifted towards a critical point during meditation by reduction of neural noise. Together with our finding that during mindfulness meditation avalanches show differences in topography relative to rest, our results show that self-regulated attention as required during meditation can serve as a control parameter of criticality in scale-free brain dynamics.

Deep brain stimulation of the nucleus basalis of Meynert attenuates early EEG components associated with defective sensory gating in patients with Alzheimer disease - a two-case study.

Alzheimer's disease (AD) is associated with deterioration of memory and cognitive function and a degeneration of neurons of the nucleus basalis of Meynert (NBM). The NBM is the major input source of acetylcholine (ACh) to the cortex. The decreasing cholinergic innervation of the cortex due to degeneration of the NBM might be the cause of loss of memory function. NBM-Deep brain stimulation (NBM-DBS) is considered to serve as a potential therapeutic option for patients with AD by supporting residual cholinergic transmission to stabilize oscillatory activity in memory-relevant circuits. However, whether DBS could improve sensory memory functions in patients with AD is not clear. Here, in a passive auditory oddball paradigm, patients with AD (N = 2) listened to repetitive background tones (standard tones) randomly interrupted by frequency deviants in two blocks with NBM-DBS OFF and then NBM-DBS ON, while age-matched healthy controls (N = 6) repeated the experiment twice. The mismatch negativity in NBM-DBS OFF significantly differed from controls in both blocks, but not under NBM-DBS, which was likely due to a pronounced P50 increase overlapping with the N1 in NBM-DBS OFF. This early complex of EEG components recovered under stimulation to a normal level as defined by responses in controls. In this temporal interval, we found in patients with NBM-DBS ON (but not with NBM-DBS OFF) and in controls a strong repetition suppression effect to standard tones - with more attenuated responses to frequently repeated standard tones. This highlights the role of NBM-DBS for sensory gating of familiar auditory information into sensory memory.

Convolutional neural networks for decoding of covert attention focus and saliency maps for EEG feature visualization.

OBJECTIVE: Convolutional neural networks (CNNs) have proven successful as function approximators and have therefore been used for classification problems including electroencephalography (EEG) signal decoding for brain-computer interfaces (BCI). Artificial neural networks, however, are considered black boxes, because they usually have thousands of parameters, making interpretation of their internal processes challenging. Here we systematically evaluate the use of CNNs for EEG signal decoding and investigate a method for visualizing the CNN model decision process. APPROACH: We developed a CNN model to decode the covert focus of attention from EEG event-related potentials during object selection. We compared the CNN and the commonly used linear discriminant analysis (LDA) classifier performance, applied to datasets with different dimensionality, and analyzed transfer learning capacity. Moreover, we validated the impact of single model components by systematically altering the model. Furthermore, we investigated the use of saliency maps as a tool for visualizing the spatial and temporal features driving the model output. MAIN RESULTS: The CNN model and the LDA classifier achieved comparable accuracy on the lower-dimensional dataset, but CNN exceeded LDA performance significantly on the higher-dimensional dataset (without hypothesis-driven preprocessing), achieving an average decoding accuracy of 90.7% (chance level = 8.3%). Parallel convolutions, tanh or ELU activation functions, and dropout regularization proved valuable for model performance, whereas the sequential convolutions, ReLU activation function, and batch normalization components reduced accuracy or yielded no significant difference. Saliency maps revealed meaningful features, displaying the typical spatial distribution and latency of the P300 component expected during this task. SIGNIFICANCE: Following systematic evaluation, we provide recommendations for when and how to use CNN models in EEG decoding. Moreover, we propose a new approach for investigating the neural correlates of a cognitive task by training CNN models on raw high-dimensional EEG data and utilizing saliency maps for relevant feature extraction.

Dimensional changes of the alveolar ridge contour of the premolar extraction site in adolescents.

PURPOSE: Premolar extraction in orthodontic therapy is common in adolescent patients. Knowledge of the tissue reaction in an extraction site is mainly based on studies with animal and adults. Thus, we aim to describe the time-dependent dimensional changes of the alveolar ridge contour of the premolar extraction site in adolescents. METHODS: Clinical data were obtained from a randomized controlled clinical trial (Universal Trial Number U1111-1132-6655), comparing treatment modalities with orthodontic space closure was initiated after 2-4 weeks (group A) and ≥ 12 weeks after tooth extraction (group B). Dental casts taken before the tooth extraction (T1) and before initiation of the space closure (T2) were digitalized with a 3D scanner and superimposed to analyze the dimensional changes of the alveolar ridge in early and later stage of wound healing. Linear mixed models were used for statistical analysis. RESULTS: Plaster models of 25 patients (mean age 15.2 years, 11 male and 14 female) with 66 extraction sites were enrolled. The average atrophic changes from tooth extraction to the early stage of wound healing (group A, n = 41) were in total 27.5% ± 11.8; labially 31.3% ± 15.1 and orally 23.6% ± 13.4. In group B (n = 25) the average atrophic changes were in total 38.6% ± 12.1; labially 46.2% ± 16.7; orally 31.3% ± 18.9. The atrophic changes between the groups in total (p = 0.031) and at the labial side (p = 0.012) were significant. The jaw affiliation was a significant parameter for all examined areas in regard to all cases (labial p = 0.019; oral p = 0.020; total p = 0.001). Atrophic changes between genders were not statistically different. CONCLUSIONS: Alveolar atrophy increased over time after extraction primarily in the lower jaw at the labial side. The main atrophy occurred in the first healing phase of the extraction socket. Thus, timely coordination is important to preserve sufficient bone levels. The atrophic changes of the alveolar ridge in adolescents parallel those reported for adults.

Biomechanical analysis of initial incisor crowding alignment in the periodontally reduced mandible using the finite element method.

AIMS: To reduce remaining plaque niches due to dental malocclusion after periodontal treatment and to avoid reinflammation of periodontitis, severe anterior crowding can be treated orthodontically. The treatment indication is motivated by aesthetic and functional needs. In this study the biomechanical behaviour of crowded lower front teeth in reduced periodontium is analysed. METHODS: Using the finite element (FE) method, a model of the mandible was constructed with an anterior crowding of 4 mm and a vertical bone loss of 4 mm in the front tooth area. A 0.3 mm (0.012″) round superelastic nickel titanium (NiTi) arch wire was fitted to an ideal positioned teeth set-up and was inserted into the brackets of teeth 34 to 44 in the crowded model. The premolars were used as the anchorage unit. Material parameters were adopted from previous investigations, including bone (homogenous, isotropic, E = 2 GPa), teeth (E = 20 GPa) and healthy periodontal ligament (PDL, bilinear elastic; E1 = 0.05 MPa; E2 = 0.2 MPa; ε12 = 7%). All simulations were compared to simulations with a physiological periodontal model to assess the effect of bone loss at teeth 42 to 32. Additionally, the influence of three arch wire materials (nonsuperelastic NiTi, superelastic NiTi and stainless steel) were analysed in a reduced model, including only brackets in position of the crowded front teeth, wire and ligatures. Wire force levels and stresses were determined to assess the influence of material variation. RESULTS: Initial tooth mobility is increased by a factor of 2.5 in case of a moderate periodontal defect. Front teeth with reduced attachment display increased strains in the periodontal ligament up to a factor of 2. Forces in the model with reduced periodontium were decreased by a factor of 2. Comparing different aligning arch wires, stainless steel appears to have the highest force and stress levels. Force levels of this alloy were 7.5 times higher than with the superelastic NiTi wire. Force levels of nonsuperelastic NiTi appeared to be 1.8 times higher than superelastic NiTi. Calculated stresses with stainless steel were 5 times higher than with the nonsuperelastic NiTi and 10 times higher than with superelastic NiTi. CONCLUSION: Periodontally reduced incisors 42 to 32 are associated with an increased load on periodontal tissue and increased level of tooth mobility during fixed orthodontic treatment. This has to be considered by reducing orthodontic force levels and by selecting mechanics that reduce the load to the tissue.

Direct Evidence for Prediction Signals in Frontal Cortex Independent of Prediction Error.

Predictive coding (PC) has been suggested as one of the main mechanisms used by brains to interact with complex environments. PC theories posit top-down prediction signals, which are compared with actual outcomes, yielding in turn prediction error (PE) signals, which are used, bottom-up, to modify the ensuing predictions. However, disentangling prediction from PE signals has been challenging. Critically, while many studies found indirect evidence for PC in the form of PE signals, direct evidence for the prediction signal is mostly lacking. Here, we provide clear evidence, obtained from intracranial cortical recordings in human surgical patients, that the human lateral prefrontal cortex evinces prediction signals while anticipating an event. Patients listened to task-irrelevant sequences of repetitive tones including infrequent predictable or unpredictable pitch deviants. The broadband high-frequency amplitude (HFA) was decreased prior to the onset of expected relative to unexpected deviants in the frontal cortex only, and its amplitude was sensitive to the increasing likelihood of deviants following longer trains of standards in the unpredictable condition. Single-trial HFA predicted deviations and correlated with poststimulus response to deviations. These results provide direct evidence for frontal cortex prediction signals independent of PE signals.

A Comparative Study on the Detection of Covert Attention in Event-Related EEG and MEG Signals to Control a BCI.

In brain-computer interface (BCI) applications the detection of neural processing as revealed by event-related potentials (ERPs) is a frequently used approach to regain communication for people unable to interact through any peripheral muscle control. However, the commonly used electroencephalography (EEG) provides signals of low signal-to-noise ratio, making the systems slow and inaccurate. As an alternative noninvasive recording technique, the magnetoencephalography (MEG) could provide more advantageous electrophysiological signals due to a higher number of sensors and the magnetic fields not being influenced by volume conduction. We investigated whether MEG provides higher accuracy in detecting event-related fields (ERFs) compared to detecting ERPs in simultaneously recorded EEG, both evoked by a covert attention task, and whether a combination of the modalities is advantageous. In our approach, a detection algorithm based on spatial filtering is used to identify ERP/ERF components in a data-driven manner. We found that MEG achieves higher decoding accuracy (DA) compared to EEG and that the combination of both further improves the performance significantly. However, MEG data showed poor performance in cross-subject classification, indicating that the algorithm's ability for transfer learning across subjects is better in EEG. Here we show that BCI control by covert attention is feasible with EEG and MEG using a data-driven spatial filter approach with a clear advantage of the MEG regarding DA but with a better transfer learning in EEG.