A neuromotor model of handwriting generation highlighting the role of basal ganglia

Handwriting (HW), unlike reaching or walking, is a high-level motor activity, engaging large parts of cortical and sub-cortical regions that include supplementary motor area(SMA), premotor area(PM), primary motor area(M1), basal ganglia(BG), cerebellum, spinal cord etc. Since each of these regions contributes to HW output in its own unique fashion, pathology of any of these regions is manifest as characteristic features in HW. For example, in Parkinson's disease, a disorder of BG, HW is marked by diminutive letter size or micrographia. Recognition of rich diagnostic value of HW had prompted a systematic study of HW and the extensive neuromotor organization that generates it. Computational modeling offers an integrative framework in which results of such studies, which come from several domains, like behavioral, imaging, etc are brought together and given a concrete shape. An integrative computational model of human motor system and BG is proposed. Dopamine deficient conditions as in PD patients are simulated in the model to reproduce PD-like handwriting features like micrographia, fluctuating velocities, jagged contour etc. The model primarily consists of a neuromotor model which is capable of learning and generating learnt strokes, and a timing model which coordinates activities in the neuromotor model. In the neuromotor model of handwritten stroke generation, stroke velocities are expressed as a Fourier-style decomposition of oscillatory neural activities. The timing network, which represents the timing action of BG, controls the events of the in the neuromotor model. The model gives a precise theory of what is loosely termed as motor preparation, involving dynamic interaction between BG and SMA. The model is further extended for multiple stroke production. The special emphasis given to BG in the models qualifies it as a candidate model for Parkinsonian handwriting. It is shown that model pathologies can capture several features of Parkinsonian handwriting like micrographia, irregular velocity profiles etc

Recognition of Anticipatory Behavior from Human EEG

Anticipation increases the efficiency of a daily task by partial advance activation of neural substrates involved in it. Single trial recognition of this activation can be exploited for a novel anticipation based Brain Computer Interface (BCI). In the current work we compare different methods for the recognition of Electroencephalogram (EEG) correlates of this activation on single trials as a first step towards building such a BCI. To do so, we recorded EEG from 9 subjects performing a classical Contingent Negative Variation (CNV) paradigm (usually reported for studying anticipatory behavior in neurophysiological experiments) with GO and NOGO conditions. We first compare classification accuracies with features such as Least Square fitting Line (LSFL) parameters and Least Square Fitting Polynomial (LSFP) coefficients using a Quadratic Discriminant Analysis (QDA) classifier. We then test the best features with complex classifiers such as Gaussian Mixture Models (GMMs) and Support Vector Machines (SVMs)

Anticipation based Brain-Computer Interfacing (aBCI)

Abstract—Anticipation increases the efficiency of daily tasks by partial advance activation of neural substrates involved in it. Previous off-line studies have shown the possibility of exploiting this activation for a Brain-Computer Interface (BCI) using electroencephalogram (EEG). In the current paper we report real-time and single trial recognition of this activation using a prototype of anticipation based BCI (aBCI). We report on-line classification accuracies with peak values of 85% and 80%, and with average values of 69.0±7.9% and 58.5±14.1% for subjects 1 and 2, respectively. Posterior off-line analysis showed improved accuracies for both subjects, with an average of 80.5±10.1% and 69.0 ± 10.5% with peak values of 95% and 85% respectively

Single Trial Recognition of Anticipatory Slow Cortical Potentials: The Role of Spatio-Spectral Filtering

Single trial recognition of slow cortical potentials (SCPs) from full-band EEG (FbEEG) faces different challenges to classical EEG such as noisy, high magnitude (~±100µV) infra slow oscillations (ISO) with f<0.1Hz and high frequency spatial noise from a variety of artifacts. We analyze offline the anticipation related SCPs recorded from 11 subjects over two days in a variation of the Contingent Negative Variation (CNV) paradigm with Go and No-go conditions in an assistive technology framework. The results suggest that widely used spatial filters such as Common Average Referencing (CAR) and Laplacian are sub-optimal for the single trial analysis of SCPs. We show that a spatial smoothing filter (SSF), which in combination with CAR enhances the spatially distributed SCP while attenuating high frequency spatial noise. We report, first, that a narrow band filter in the range [0.1 1]Hz captures anticipation related SCP better and effectively reduces ISOs. Second, the SSF in combination with CAR outperforms CAR-alone and Laplacian spatial filters. Third, we compare linear and quadratic classifiers calculated using optimally filtered Cz electrode potentials and report that the best methods resulted in single trial classification accuracies of 83±4%, where classifiers were trained on day 1 and tested using data from day 2, to ensure generalization capabilities across days (1-7 days)

Single trial analysis of slow cortical potentials: A study on anticipation related potentials

Objective. Abundant literature suggests the use of Slow Cortical Potentials (SCPs) in a wide spectrum of basic and applied neuroscience areas. Due to low signal to noise ratio, often these potentials are studied using grand-average analysis, which conceal trial-to-trial information. Moreover, most of the single trial analysis methods in literature are based on classical- electroencephalogram (EEG) features ([1–30] Hz) and are likely to be unsuitable for SCPs that have different signal properties (such as signal’s spectral content in the range [0.2–0.7]Hz). In this paper we provide insights into the selection of appropriate parameters for spectral and spatial filtering. Approach. To this end, we study anticipation related SCPs recorded using a web-browser application protocol using full-band EEG (FbEEG) setup from 11 subjects on two different days. Main results. We first highlight the role of a bandpass with [0.1–1.0]sHz in comparison with common practices (e.g., either with full DC, just a lowpass, or with a minimal highpass cut-off around 0.05Hz). Second, we suggest that a combination of spatial-smoothing filter (SSF) and common average reference (CAR) is more suitable than the spatial filters often reported in literature (e.g., re-referencing to an electrode, Laplacian or CAR alone). Third, with the help of these preprocessing steps, we demonstrate the generalization capabilities of linear classifiers across several days (AUC of 0.88 ± 0.05 on average with a minimum of 0.81 ± 0.03 and a maximum of 0.97 ± 0.01). We also report the possibility of further improvement using a Bayesian fusion technique applied to electrode-specific classifiers. Significance. We believe the suggested spatial and spectral preprocessing methods are advantageous for grand-average and single trial analysis of SCPs obtained from EEG, MEG as well as for electrocorticogram (ECoG). The use of these methods will impact basic neurophysiological studies as well as the use of SCPs in the design of neuroprosthetics

Fast Recognition of Anticipation Related Potentials

Anticipation increases the efficiency of daily tasks by partial advance activation of neural substrates involved in it. Here we develop a method for the recognition of electroencephalogram (EEG) correlates of this activation as early as possible on single trials which is essential for Brain-Computer Interaction (BCI). We explore various features from the EEG recorded in a Contingent Negative Variation (CNV) paradigm. We also develop a novel technique called Time Aggregation of Classification (TAC) for fast and reliable decisions that combines the posterior probabilities of several classifiers trained with features computed from temporal blocks of EEG until a certainty threshold is reached. Experiments with 9 naive subjects performing the CNV experiment with GO and NOGO conditions with an inter-stimulus interval of 4 s show that the performance of the TAC method is above 70% for four subjects, around 60% for two other subjects, and random for the remaining subjects. On average over all subjects, more than 50% of the correct decisions are made at 2 s, without needing to wait until 4 s

Discriminative Channel Selection Method for the Recognition of Anticipation related Potentials from CCD estimated Cortical Activity

Recognition of brain states and subject's intention from electroencephalogram (EEG) is a challenging problem for brain-computer interaction. Signals recorded from each of EEG electrodes represent noisy spatio-temporal overlapping of activity arising from very diverse brain regions. However, un-mixing methods such as Cortical Current Density (CCD) can be used for estimating activity of different brain regions. These methods not only improve spatial resolution but also signal to noise ratio, hence the classifiers computed using this activity may ameliorate recognition performances. However, these methods lead to a multiplied number of channels, leading to the question -- ``How to choose relevant and discriminant channels from a large number of channels?''. In the current paper we present a channel selection method and discuss its application to the recognition of anticipation related potentials from surface EEG channels and CCD estimated cortical potentials. We compare the classification accuracies with previously reported performances obtained using Cz electrode potentials of 9 subjects (3 experienced + 6 naive). As hypothesised, we observed improvements for most subjects with channel selection method applied to CCD activity as compared to surface-EEG channels and baseline performances. This improvement is particularly significant for subjects who are naive and did not show a clear pattern on ERP grand averages

The use of brain-computer interfacing for ambient intelligence

This paper is aimed to introduce IDIAP Brain Computer Interface (IBCI) research that successfully applied Ambience Intelligence (AmI) principles in designing intelligent brain-machine interactions. We proceed through IBCI applications describing how machines can decode and react to the human mental commands, cognitive and emotive states. We show how effective human-machine interaction for brain computer interfacing (BCI) can be achieved through, 1) asynchronous and spontaneous BCI, 2) shared control between the human and machine, 3) online learning and 4) the use of cognitive state recognition. Identifying common principles in BCI research and ambiance intelligence (AmI) research, we discuss IBCI applications. With the current studies on recognition of human cognitive states, we argue for the possibility of designing empathic environments or devices that have a better human like understanding directly from brain signals

Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis

Brain‐computer interfaces (BCIs) can provide sensory feedback of ongoing brain oscillations, enabling stroke survivors to modulate their sensorimotor rhythms purposefully. A number of recent clinical studies indicate that repeated use of such BCIs might trigger neurological recovery and hence improvement in motor function. Here, we provide a first meta‐analysis evaluating the clinical effectiveness of BCI‐based post‐stroke motor rehabilitation. Trials were identified using MEDLINE, CENTRAL, PEDro and by inspection of references in several review articles. We selected randomized controlled trials that used BCIs for post‐stroke motor rehabilitation and provided motor impairment scores before and after the intervention. A random‐effects inverse variance method was used to calculate the summary effect size. We initially identified 524 articles and, after removing duplicates, we screened titles and abstracts of 473 articles. We found 26 articles corresponding to BCI clinical trials, of these, there were nine studies that involved a total of 235 post‐stroke survivors that fulfilled the inclusion criterion (randomized controlled trials that examined motor performance as an outcome measure) for the meta‐analysis. Motor improvements, mostly quantified by the upper limb Fugl‐Meyer Assessment (FMA‐UE), exceeded the minimal clinically important difference (MCID=5.25) in six BCI studies, while such improvement was reached only in three control groups. Overall, the BCI training was associated with a standardized mean difference of 0.79 (95% CI: 0.37 to 1.20) in FMA‐UE compared to control conditions, which is in the range of medium to large summary effect size. In addition, several studies indicated BCI‐induced functional and structural neuroplasticity at a subclinical level. This suggests that BCI technology could be an effective intervention for post‐stroke upper limb rehabilitation. However, more studies with larger sample size are required to increase the reliability of these results

Increasing upper limb training intensity in chronic stroke using embodied virtual reality: a pilot study.

Technology-mediated neurorehabilitation is suggested to enhance training intensity and therefore functional gains. Here, we used a novel virtual reality (VR) system for task-specific upper extremity training after stroke. The system offers interactive exercises integrating motor priming techniques and embodied visuomotor feedback. In this pilot study, we examined (i) rehabilitation dose and training intensity, (ii) functional improvements, and (iii) safety and tolerance when exposed to intensive VR rehabilitation. Ten outpatient stroke survivors with chronic (&gt;6 months) upper extremity paresis participated in a ten-session VR-based upper limb rehabilitation program (2 sessions/week). All participants completed all sessions of the treatment. In total, they received a median of 403 min of upper limb therapy, with 290 min of effective training. Within that time, participants performed a median of 4713 goal-directed movements. Importantly, training intensity increased progressively across sessions from 13.2 to 17.3 movements per minute. Clinical measures show that despite being in the chronic phase, where recovery potential is thought to be limited, participants showed a median improvement rate of 5.3% in motor function (Fugl-Meyer Assessment for Upper Extremity; FMA-UE) post intervention compared to baseline, and of 15.4% at one-month follow-up. For three of them, this improvement was clinically significant. A significant improvement in shoulder active range of motion (AROM) was also observed at follow-up. Participants reported very low levels of pain, stress and fatigue following each session of training, indicating that the intensive VR intervention was well tolerated. No severe adverse events were reported. All participants expressed their interest in continuing the intervention at the hospital or even at home, suggesting high levels of adherence and motivation for the provided intervention. This pilot study showed how a dedicated VR system could deliver high rehabilitation doses and, importantly, intensive training in chronic stroke survivors. FMA-UE and AROM results suggest that task-specific VR training may be beneficial for further functional recovery both in the chronic stage of stroke. Longitudinal studies with higher doses and sample sizes are required to confirm the therapy effectiveness. This trial was retrospectively registered at ClinicalTrials.gov database (registration number NCT03094650 ) on 14 March 2017