NEUROTECHNIX 2014 Abstracts


Full Papers
Paper Nr: 4
Title:

Modeling White-matter Fiber-orientation Uncertainty for Improved Probabilistic Tractography

Authors:

Adelino R. Ferreira da Silva

Abstract: Tractography uses fiber-orientation estimates to trace the likely paths of white-matter tracts through the brain, in order to map brain connectivity non-invasively. In this paper, we propose a novel probabilistic framework for modeling fiber-orientation uncertainty and improve probabilistic tractography. The main innovation in the present formulation consists in coupling a particle filtering process with a clustered-mixture model approach to model directional data. Mixtures of von Mises-Fisher (vMF) distributions are used to support the probabilistic estimation of intravoxel fiber directions. The fitted parameters of the clustered vMF mixture at each voxel are then used to estimate white-matter pathways using particle filtering techniques. The technique is validated on simulated as well as on real human brain data experiments.

Paper Nr: 9
Title:

Development of Micro-channel Arrays for Peripheral Nerve Recording

Authors:

David J. Edell, Ronald R. Riso and Hugh Herr

Abstract: MicroTube Array (MTA) technology was developed to create an axon regeneration interface for exchanging motor and sensory data with residual nerves. Future clinical application will include sensory-motor transducers for individuals with limb amputation. In this pilot study, a small matrix (9) of MTAs 1, 3 and 5mm long with either 99um, 200um or 287um diameter MicroTubes (MTs) filling nerve cuffs of 3mm in diameter were implanted in tibial nerve of NZW rabbits and histologically evaluated after more than 6 months post-op. Full regeneration was observed in all 3 lengths for 287um MTAs, and for all three diameters of MTs with 1mm length. The remaining implants were mechanically dislodged during the healing phase. A second implant set was designed to include 12 platinum-iridium wire electrodes direct wired to a percutaneous connector. Successful recordings of useful amplitudes were observed during reflex righting behaviour for over 2 years before the anticipated wire breakage ended the experiments.

Paper Nr: 13
Title:

EEG and Eye-Tracking Integration for Ocular Artefact Correction

Authors:

P. Rente Lourenço, W. W. Abbott and A. A. Faisal

Abstract: Electroencephalograms (EEG) are a widely used brain signal recording technique. The information conveyed in these recordings can be an extremely useful tool in the diagnosis of some diseases and disturbances, as well as in the development of non-invasive Brain-Machine Interfaces (BMI). However, the non-invasive electrical recording setup comes with two major downsides, a. poor signal-to-noise ratio and b. the vulnerability to any external and internal noise sources. One of the main sources of artefacts are eye movements due to the electric dipole between the cornea and the retina. We have previously proposed that monitoring eye-movements provide a complementary signal for BMIs. He we propose a novel technique to remove eye-related artefacts from the EEG recordings. We couple Eye Tracking with EEG allowing us to independently measure when ocular artefact events occur and thus clean them up in a targeted manner instead of using a "blind" artefact clean up correction technique. Three standard methods of artefact correction were applied in an event-driven, supervised manner: 1. Independent Components Analysis (ICA), 2. Wiener Filter and 3. Wavelet Decomposition and compared to "blind" unsupervised ICA clean up. These are standard artefact correction approaches implemented in many toolboxes and experimental EEG systems and could easily be applied by their users in an event-driven manner. Already the qualitative inspection of the clean up traces show that the simple targeted artefact event-driven clean up outperforms the traditional “blind” clean up approaches. We conclude that this justifies the small extra effort of performing simultaneous eye tracking with any EEG recording to enable simple, but targeted, automatic artefact removal that preserves more of the original signal.

Paper Nr: 14
Title:

Developing a Novel fMRI-Compatible Motion Tracking System for Haptic Motor Control Experiments

Authors:

M. Rodríguez, A. Sylaidi and A. A. Faisal

Abstract: Human neuroimaging can play a key role in addressing open questions in motor neuroscience and embodied cognition by linking human movement experiments and motor psychophysics to the neural foundation of motor control. To this end we designed and built fMOVE, an fMRI-compatible motion tracking system that captures 3DOF goal-directed movements of human subjects within a neuroimaging scanner. fMOVE constitutes an ultra-low-cost technology, based on a zoom lens high-frame rate USB camera and, our adaptation library for camera-based motion tracking and experiment control. Our motion tracking algorithm tracks the position of markers attached to a hand-held object. The system enables to provide the scanned subjects a closed-loop real time visual feedback of their motion and control of complex, goal-oriented movements. The latter are instructed by simple speed-accuracy tasks or goal-oriented object manipulation. The system’s tracking precision was tested and found within its operational parameters comparable to the performance levels of a scientific grade electromagnetic motion tracking system. fMOVE thus offers a low-cost methodological platform to re-approach the objectives of motor neuroscience by enabling ecologically more valid motor tasks in neuroimaging studies.

Paper Nr: 23
Title:

Prediction of Movements by Online Analysis of Electroencephalogram with Dataflow Accelerators

Authors:

Hendrik Wöhrle, Johannes Teiwes, Marc Tabie, Anett Seeland, Elsa Andrea Kirchner and Frank Kirchner

Abstract: Brain Computer Interfaces (BCIs) allow to use psychophysiological data for a large range of innovative applications. One interesting application for rehabilitation robotics is to modulate exoskeleton controls by predicting movements of a human user before they are actually performed. However, usually BCIs are used mainly in artificial and stationary experimental setups. Reasons for this are, among others, the immobility of the utilized hardware for data acquisition, but also the size of the computing devices that are required for the analysis ofthe human electroencephalogram. Therefore, mobile processing devices need to be developed. A problem is often the limited processing power of these devices, especially if there are firm time constraints as in thecase of movement prediction. Field programmable gate array (FPGA)-based application-specific dataflow accelerators are a possible solution here. In this paper we present the first FPGA-based processing system that is able to predict upcoming movements by analyzing the human electroencephalogram. We evaluate the system regarding computation time and classification performance and show that it can compete with a standard desktop computer.

Paper Nr: 33
Title:

Predicting Wrist Movement Trajectory from Ipsilesional ECoG in Chronic Stroke Patients

Authors:

Martin Spüler, Wolfgang Rosenstiel and Martin Bogdan

Abstract: Recently, there have been several approaches to utilize a Brain-Computer Interface (BCI) for chronic stroke patients. The prediction of movement trajectory based on recorded brain activity could thereby help to improve BCI-guided stroke rehabilitation or could be used for control of an assistive device, like an orthosis or a robotic arm. One problem in predicting movement trajectory in stroke patients are compensatory movements, which make it difficult to link specific brain activity to movement intention. In this paper we compare different methods for trajectory prediction and show how Canonical Correlation Analysis (CCA) can be used to predict movement trajectories. Based on the results, we argue that the resulting trajectory prediction is closer to the actual movement intention. We further show how the transformation matrices obtained by CCA can be interpreted and discuss how this interpretation might be useful to get information regarding compensatory movements in stroke and the underlying patterns of brain activity.

Short Papers
Paper Nr: 7
Title:

CyberBrain - A Preliminary Experience on Non Human Primate

Authors:

M. Piangerelli, A. Paris and P. Romanelli

Abstract: The study of abnormal electrical activity of the brain, such as epilepsy, is attracting more and more interest for its wide impact on the population. Intracranial EEG recording (electrocorticogaphy; EcoG) and direct cortical stimulation (DCS) are, nowadays, the most accurate and reliable techniques to map cortical function and to identify the boundaries of an epileptic focus. In this work we present the preliminary testing of intra-operative ECoG and DCS performed in a non-human primate using a new custom-made fully-implantable wireless 16-channels device (Patent Number: WO2012143850), called ECOGW-16E. This fully-integrated device, housed in a compact hermetically sealed Polyetheretherketone (PEEK) enclosure, exploits the newly available Medical Implant Communication Service band (MICS: 402-405 MHz). ECOGW-16E is wirelessly rechargeable using a special designed cage for recharge, developed in accordance with guidelines for accommodation of animals by Council of Europe.

Paper Nr: 12
Title:

Accuracy and Precision of the Tobii X2-30 Eye-tracking under Non Ideal Conditions

Authors:

A. Clemotte, M. Velasco, D. Torricelli, R. Raya and R. Ceres

Abstract: This document describes a methodology for the measurement of accuracy and precision of a remote eye tracker, the Tobii X2-30, under non ideal condition. The test was performed with 10 people. The results are: 2.46 and 1.91 degrees for the accuracy and precision respectively. The results can be used to establish the target size on the screen.

Paper Nr: 15
Title:

Detrended-Fluctuation-Analysis (DFA) and High-Frequency-Oscillation (HFO) Coefficients and Their Relationship to Epileptic Seizures

Authors:

Fabrício Henrique Simozo, João Batista Destro Filho and Luiz Otávio Murta Junior

Abstract: We tested the applicability of methods based on Detrended Fluctuation Analysis and HFO detection to the analysis of EEG signals from patients diagnosed with epilepsy, in order to test how efficient these methods would behave in a seizure prediction application. We were able to statistically distinguish the coefficients estimated in the pre-ictal period from the coefficients obtained on the inter-ictal period, suggesting that the methods can be used to the development of seizure detection algorithms.

Paper Nr: 16
Title:

Repeated Anodal tDCS Coupled with Cognitive Training for Patients with Severe Traumatic Brain Injury - A Pilot RCT

Authors:

Marcin Leśniak, Katarzyna Polanowska and Joanna Seniów

Abstract: Anodal transcranial direct current stimulation (A-tDCS) – a non-invasive neuromodulation technique has been shown to improve cognitive performance both in healthy subjects and patients with brain damage. The aim of this project was to determine whether repeated A-tDCS of the left dorso-lateral prefrontal cortex (DLPFC) could enhance efficacy of treatment of memory and attention in patients with severe traumatic brain injury (TBI). Twenty-three adult patients, 4-92 months post severe TBI with subsequent cognitive deficits were randomly allocated to two groups. The experimental group received 15 sessions of A-tDCS (10 min; 1 mA; in the DLPFC), followed by cognitive training. Controls received A-tDCS for 30 s (sham condition) with the same rehabilitation program. A battery of memory and attention tests were used as outcome measures. Participants were tested twice before beginning rehabilitation (to control for spontaneous recovery), immediately after rehabilitation completion, and four months later. Tests scores in both groups were similar at three weeks before and immediately before treatment, and spontaneous improvement was minimal. After treatment, the experimental group exhibited larger effect sizes in six out of eight cognitive outcome measures, but they were not significantly different from controls. At follow-up, scores achieved by patients from experimental group remained higher but the differences were insignificant. In conclusion, our study did not provide sufficiently strong evidence to support the efficacy of repeated A-tDCS for enhancing effects of memory and attention treatment in patients after severe TBI.

Paper Nr: 18
Title:

Growth Mechanism of Rat Dorsal Root Ganglion Neurons on Slope Substrate

Authors:

Xiao Li, Yuanyuan Wang, Qi Xu, Fang Chen and Jiping He

Abstract: Neural response to topography depends on the dimensions and shapes of physical features. Most researchers focused on fabricating different grooves and ridges to study cell adhesion, spreading, alignment, and morphological changes. Very few papers report about how sloped substrate influences the behavior of neural cells. In this paper, we made a preliminary experiment to test the reaction of neuronal growth processes to different slopes. We found that all DRG cells’ axons couldn’t grow across 90 degree slope with 198 μm height. A few axons grew across 90 degree slope with 50 μm height. In addition, we also found that DRG cells showed preference to grow uphill rather than downhill. In future, we will make more detailed experiments to study the mechanism of slope modulation. This study will be helpful for the construction of nerve regenerating scaffolds and neural interface.

Paper Nr: 20
Title:

Activities of Daily Living in Healthy Adults and Children - Reliability of Registrations with Multiple Body Worn Sensors

Authors:

Ryanne Lemmens, Henk Seelen, Yvonne Janssen-Potten, Annick Timmermans, Marlous Schnackers, Annet Eerden, Richard Geers and Rob Smeets

Abstract: 1 OBJECTIVES Patients with stroke or cerebral palsy often encounter arm-hand problems during daily life. Assessment is important to determine the progress of arm-hand performance in patients during rehabilitation, and to ascertain the effectiveness of therapies. Many instruments are available to assess capacity or perceived performance, but instruments assessing actual performance are scarce (Lemmens et al., 2012). Inertial sensors may be used to assess actual performance. However, signal reliability during execution of activities of daily living (ADL) should be determined first. Aim of this study was to examine the reliability of the data of upper extremity skill performance in a standardized setting, in both healthy adults and healthy children, registered using a combination of multiple body worn sensors. 2 METHODS In this non-randomised cross-sectional study, both healthy adults (aged > 50 years) and healthy children were included. Because motor control processes may mature with age, resulting in differences in skill execution, the children were divided into two age groups, i.e. 6-11 years and 12-18 years. Four 9-DOF sensor devices, each containing a tri-axial accelerometer, gyroscope and magnetometer were attached to the dominant hand, wrist, upper arm and chest of participants. Data were registered during the execution of 5 repetitions of 2 tasks, i.e. ‘drinking from a cup’ and ‘eating with knife and fork’. Tasks were first performed without extensive instructions (e.g. with the instruction: “drink from the cup.”), and subsequently with extensive instructions on how to perform the task (e.g. “reach to the cup, grasp it, bring it to your mouth, take a sip, put the cup back on the table and go back to your starting position.”). Signals were filtered with a 4th order zero-time lag low-pass Butterworth filter (cut off frequency of 1.28 Hz). Repetitions of each specific task were identified and intra-class correlation coefficients (ICC) for each sensor and signal type were determined as a measure of reliability, both within and between subjects. For every person, a mean ICC was measured. Since data was not normally distributed, medians were calculated. The ICCs were classified based on the kappa statistic classification of Landis and Koch, i.e. ICC between 0.8-1.0=very good; 0.6-0.8=good; 0.4-0.6=moderate; 0.2-0.4=fair; <0.2=slight (Landis and Koch, 1977). 3 RESULTS Thirty adults were included (14 women, 16 men, mean age 58.0 ± 5.1 years), 16 children aged between 6-11 years (9 girls, 7 boys, mean age 8.5 ± 1.7 year) and 16 children aged between 12 and 18 years (8 girls, 8 boys, mean age 14.6 ± 1.5 years). Figure 1 displays within-subject reliability of the skills drinking and eating for both the adults and the children. With regard to the within subject reliability, the median ICC’s were good for the skill eating and very good for the skill drinking. Reliability was better for the skill performed with instruction compared to the skill performed without instruction, especially for the skill eating. Furthermore it can be seen that the children aged between 12-18 years showed a slightly higher reliability compared to the children aged between 6-12 years. Figure 2 displays between-subject reliability of the skills drinking and eating for both the adults and the children. Between-subject reliability was good to very good for both skills performed by adults. The skills drinking performed by the children had a very good reliability whereas the skill eating had a fair to moderate reliability in the youngest children, and a good reliability in the older children. Especially for the skill eating, a big difference was seen regarding the reliability of the performance without instruction compared to the performance with instruction. For the skill drinking, reliability was comparable between younger children and the older children whereas for the skill eating, performance of the older children had a higher reliability. 4 DISCUSSION Overall, the skill drinking as well as the skill eating had a good to very good within-subject reliability in both adults and children. Performance with instruction had a higher reliability compared to performance without instruction. By giving instructions about how to perform the task, the variability in execution of the task was reduced, thereby increasing the reliability. The performance of the skill drinking had a higher reliability compared to the skill eating. This can be explained by the complexity of the skills, i.e. drinking is a rather simple skill, which cannot be performed in many ways, whereas the skill eating consists of more sub movements and can, in addition, be performed in many different ways. Between-subject reliability of the skill eating was relatively low, especially for the performance without instruction in the youngest children. This can be explained by the fact that many children did not use the knife to cut the food, whereas other children had difficulties manipulating the knife. For the performance with instruction, they were told how to use the knife. In conclusion, we have shown that a combination of multiple body worn sensors is able to reliably register activities of daily living in healthy adults as well as in healthy children. Future research will focus on the investigation of signal reliability during activities of daily living performed by patients and in a daily life setting.

Paper Nr: 21
Title:

Sensor-based Pattern Recognition Identifying Complex Upper Extremity Skills

Authors:

Ryanne Lemmens, Yvonne Janssen-Potten, Annick Timmermans, Rob Smeets and Henk Seelen

Abstract: 1 OBJECTIVES Objectively quantifying actual arm-hand performance is very important to evaluate arm-hand therapy efficacy in patients with neurological disorders. Currently, objective assessments are limited to evaluation of ‘general arm hand activity’, whereas monitoring specific arm-hand skills is not available yet. Instruments to identify skills and determine both amount and quality of actual arm-hand use in daily life are lacking, necessitating the development of a new measure. To identify skills, pattern recognition techniques can be used. Commonly used pattern recognition approaches are: statistical classification, neural networks, structural matching and template matching (Jain et al., 2000). The latter is used in the present study, aiming to provide proof-of-principle of identifying skills, illustrate this for the skill drinking in a standardized setting and daily life situation in a healthy subject. 2 METHODS Four sensor devices, each containing a tri-axial accelerometer, tri-axial gyroscope and tri-axial magnetometer were attached to the dominant hand, wrist, upper arm and chest of participants. Thirty healthy individuals performed the skill drinking 5 times in a standardized manner, i.e. with similar starting position and instruction about how to perform the skill. In addition, for one person a 30 minute registration in daily life including multiple skills (of which 4 times the skill drinking) was made. Signals were filtered with a 4th order zero-time lag low-pass Butterworth filter (cut off frequency: 2.5 Hz). Data analysis consisted of the following steps: 1) temporal delimitation of each of the five attempts of the skill drinking, i.e. identifying the start and endpoint of each attempt recorded; 2) normalization of the signals in the time domain in order to correct for (small) variations due to differences in speed of task execution; 3) averaging signal matrices from the five attempts of each individual person to obtain the individual template, i.e. the underlying ensemble averaged signal matrix per task per individual; averaging signal matrices from the individual templates of multiple persons, to create a generic template; 4) identification of dominant sub phases of templates, within a specific task, using Gaussian-based linear envelope decomposition procedures; 5) recognition of specific skill execution among various skills performed daily, i.e. searching for template occurrence among signal recordings gathered in a standardized setting and a daily life condition, using feature extraction and pattern recognition algorithms based on 2D convolution. Cross-correlation coefficients were calculated to quantify goodness-of-fit. 3 RESULTS Performance of the skill drinking was identified unambiguously (100%) in de standardized setting (figure 1a). For the templates consisting of the complete skill, mean cross-correlation was 0.93 for the individual template and 0.79 for the generic template. For the templates consisting of sub-phases, mean cross-correlations ranged between 0.89 and 0.99 for the individual template and between 0.78 and 0.86 for the generic template. In the daily life registration, all instances at which drinking was performed, were recognized with the template consisting of the complete skills (mean cross-correlation: 0.51) (figure 1b). However, also five false-positive findings were present (mean cross-correlation: 0.46). Using the template consisting of the sub phases, in general the skill drinking was identified (cross-correlation ranging between 0.62 and 0.82), but some sub phases were not recognized correctly. A false-positive finding occurred frequently for sub phase 1 and sporadic for the other sub phases (mean cross-correlation between 0.55 and 0.92). Regarding the combination of sub phases, no false-positive findings were found. 4 DISCUSSION Using this method, it is possible to identify a specific skill amongst multiple skills, both in a standardized setting and in a daily life registration. The long-term aim is to use this method to a) identify which arm-hand skills are performed during daily life by individuals, b) determine the quantity of skill execution, i.e. amount of use, and c) determine the quality of arm-hand skill performance. At the moment, as far as we know, no such instrument is available. There are however many instrument being developed using many different pattern recognition techniques. Leutheuser et al, for example, used a feature set of four time domain features and two frequency domain features and a combination of classification systems to distinguish between activities like vacuuming, sweeping, sitting, standing, bicycling, ascending/descending stairs and walking (Leutheuser et al., 2013). Future research will firstly focus on optimizing the method described in this study, and thereafter focus on applying this method for more skills, in neurological patients and in natural living situations. REFERENCES Jain A.K., Duin R.P.W. & Mao J. (2000) Statistical Pattern Recognition: A Review. IEEE Trans Pattern Analysis and Machine Intelligence, 22. Leutheuser H., Schuldhaus D. & Eskofier B.M. (2013) Hierarchical, Multi-sensor based Classification of Daily Life Activities: Comparison with State-of-the-art Algorithms using a Benchmark Dataset. PlosOne, 8 .

Paper Nr: 22
Title:

Platform for Multimodal Signal Acquisition for the Control of Lower Limb Rehabilitation Devices

Authors:

Douglas Ruy Soprani S. Araujo, Thomaz Rodrigues Botelho, Camila Rodrigues C. Carvalho, Anselmo Frizera, Andre Ferreira and Eduardo Rocon

Abstract: Patients with some sort of motor disability may benefit from robotic rehabilitation since it can provide more control, accuracy and variety of training modes. This enhances the efficiency of the rehabilitation and, therefore, the recovery of the patient. Assistive devices, like exoskeletons or orthoses, can make use of physiological data, such as electromyography (EMG) and electroencephalography (EEG), in order to detect the movement intention. Combination of data can potentially improve the adaptability of assistive devices with respect to the individual demands. Different methods can be applied depending on the neuromuscular disorder, therapy or assistive device. In this work, we present a multimodal interface which integrates EEG, EMG and inertial sensors (IMU) signals. Experiments were conducted with healthy subjects performing lower limb motor tasks. The aim of the proposed system is to analyze the movement intention (EEG signal), the muscle activation (EMG signal) and the limb motion onset (IMU signal). An experimental protocol is proposed. The results obtained showed that the system is capable to acquire and process the biological signals synchronously. Results indicated that the system is able to identify the movement intention, based on the EEG signal, the movement anticipation, based on the muscle activation, and the limb motion onset.

Paper Nr: 29
Title:

Simultaneously Probing Functional and Structural Brain Connectivity in Real-time - Fibernavigator: An Interactive Tool for Brain Visualization

Authors:

Maxime Chamberland, Maxime Descoteaux, Kevin Whittingstall and David Fortin

Abstract: The human brain can be viewed as a collection of networks. Those highly specialized networks can be referred to as a set of nodes (gray matter functional areas) linked together by edges (for example white matter axonal structure). Functional MRI (fMRI) can provide 4D whole-brain images that indicates changes in cortical blood flow, volume and oxygen ratio as well (Blood-Oxygenation-Level-Dependant or BOLD signal) caused by cerebral activity across time (Bandettini et al. 1993; Kwong et al. 1992; Turner 1992). The spontaneous low fluctuations (< 0.1 Hz) present in the BOLD signal allow the detection temporally correlated spatial patterns, also known as Resting State Networks (RSNs) when the brain is at rest (Biswal et al. 1995; Damoiseaux et al. 2006). A common method of obtaining those networks is to extract the BOLD time course from an a priori region of interest (ROI) and perform the temporal correlation with all other voxels of the brain. The result is a correlation map or a functional connectivity map based on the location of the seed ROI. Some have proposed a tool for voxel-wise brain connectivity visualization but it often requires the pre-calculation of a correlation matrix to be held in memory (Dixhoorn 2012). Great effort was also made towards GPU implementation of functional connectivity exploration (Eklund et al. 2011) However, the proposed software restrict the user from placing their reference ROI directly into the 3D space which greatly reduces the level of interactivity. Another tool was proposed for neurosurgical application which quickly allows the user to interrogate data for pre-surgical planning (Böttger et al. 2011). Here, the user is forced to move the ROI solely on 2D anatomical slices, thus only revealing activations present on those selected slices. In this work, we propose an interactive tool for the exploration of functional connectivity in a fully 3D fashion, which can be coupled with our existing real-time fiber tracking module inside the Fibernavigator (Chamberland et al. 2014). Using a healthy volunteer dataset, we qualitatively demonstrate how both functional and structural modules can be merged together for efficient brain mapping exploration.

Paper Nr: 30
Title:

Delayed Feedback Control of Oscillations in a Spiking Neural Network Model of Aberrant Brain Dynamics

Authors:

Ioannis Vlachos and Arvind Kumar

Abstract: Open-loop methods for deep-brain stimulation have been effective in controlling aberrant activity associated with various neurological disorders such as Parkinson's disease. Recently, adaptive control strategies have emerged, which promise to increase the efficacy of these existing stimulation methods. Here, we investigate the effects of closed-loop control schemes in networks of spiking neurons that operate in a synchronous irregular regime. In this regime the population activity is highly regular, despite the fact that individual neurons fire stochastically. These oscillations are known to be robust compared to synchronous regular activity and are not easily affected by noise or heterogeneity. We design an appropriate control strategy, based on delayed state-feedback to quench these stochastic oscillations. We also show that our control protocol is able to restore the network transfer function thus overcoming the undesired side-effects of existing methods.

Paper Nr: 34
Title:

Preliminary Study to Detect Gait Initiation Intention Through a BCI System

Authors:

Daniel Planelles, Enrique Hortal, Eduardo Iánez, Álvaro Costa, Andrés Úbeda and José M. Azorín

Abstract: In this paper is presented an experiment designed to detect the will to perform several steps forward (as walking onset) before it occurs using the electroencephalographic (EEG) signals collected from the scalp. The preliminary results from five users have been presented. In order to improve the quality of the signals acquired some different spatial filters are applied and compared. In the future, the improved Brain-Computer Interface of this paper will be used as part of the control system of an exoskeleton attached to the lower limb of people with incomplete and complete spinal cord injury to initiate their gait cycle.

Posters
Paper Nr: 3
Title:

WLAN Interface for a Wireless EEG System

Authors:

E. Velarde-Reyes and F. Martin-Gonzalez

Abstract: A WLAN interface for a Wireless EEG System is presented in this paper. Selection of broadcasting band, available hardware, and connection algorithm to use are discussed before making a choice. Two alternatives were explored: Wireless EEG Device (Holter) and its Server communicate with each other within the same physical network, and from a complex network like the Internet. Results of experimental tests carried out on the prototype demonstrate the functionality of the implemented interface.

Paper Nr: 24
Title:

The Use and Know-how of ICT-technology in Different Age Groups

Authors:

Leena Korpinen, Rauno Pääkkönen and Fabriziomaria Gobba

Abstract: When developing various ICT solutions to support people’s well-being, the systems are quite often based on the use of computers or smart phones. However, in different age groups, the skills to use ICT can vary; therefore, not all people can use new technical systems. The aim of this paper was to investigate the self-reported use and know-how of the ICT-technology in different age groups and using the answers to the following questions: ‘how often do you use a desktop computer at leisure?’ and ‘how well do you know the desktop computer?’. The study was carried out as a cross-sectional study by posting the questionnaire to 15,000 working-age Finns. To the question ‘how well do you know the desktop computer?’, 22% of the 20-30 age group answered ‘very well’ and 19.1% of the 31-40 age group also replied ‘very well’. In the 41-50 age group, the value was 15.7%, and in the age group 51-60, the value was 10.6%. In the future, when new well-being ICT technology is developed, it is important to take into account that older people do not know as much about ICT as younger people.

Paper Nr: 26
Title:

Chirp Analyzer for Estimating Non-stationary Auditory Signals

Authors:

Y. García-Puente, P. Prado-Gutiérrez and E. Martínez-Montes

Abstract: The development of clinical tools for objectively measuring the auditory temporal processing is important for the early diagnosis of speech pathologies related to hearing impairments. In this regards, the use of mathematical models and signal processing techniques is essential to characterize the electrophysiological responses to speech-related auditory stimuli. Objectives: To propose a chirp analyzer (CA) method for the reliable estimation of non-stationary auditory electrophysiological responses, and evaluate its potential applicability by comparing it with the known time-frequency methodologies Short Time Fourier Transform and Morlet wavelet transform. Methods: Using simulated and real data, we compare the robustness and reliability of the three methods for different response forms, as well as with different physiological response delay. Results: In general, the three methods were able to recover the shape of the simulated and real physiological response when there are low levels of noise and response latency is small. The CA is faster and more robust to noise; while complex continuous Morlet wavelet transform is very sensitive to noise but more reliable when the response appears with a considerable delay. Conclusions: Results suggest that the CA is a promising tool for estimating non-stationary auditory electrophysiological responses, although optimal estimation might be achieved with a combination of the three techniques. In any case, future studies should be carried out pursuing a more thorough validation of these methodologies.

Paper Nr: 27
Title:

Preliminary Comparison of Classifiers to Detect Spatio-spectral Patterns of Epileptic Seizures via PARAFAC Decomposition

Authors:

Marlis Ontivero-Ortega, Yalina García-Puente and Eduardo Martínez-Montes

Abstract: The automatic detection of epileptic seizures from EEG recording is very important for clinical diagnosis and monitoring and has become an issue of major scientific and technological interest. In this work, we use the spatio-spectral features extracted via multidimensional PARAFAC analysis of the EEG for seizure detection. This is a subject-specific approach which only requires extracting one component explaining a seizure’s space-time-frequency pattern. Then, we propose a simple adaptive zero-training technique (AZT) to classify the seizures, with the additional advantages of being fast and able to be used online. The performance of this technique is evaluated by comparing its accuracy, sensitivity and specificity with those obtained from known pattern recognition methodologies (LDA, SVM, k-Means), on EEG recordings of two epileptic pediatric patients. Results showed that the new method offers the highest sensitivity for different segments’ length, although small segments lead to an increase of the false positives rate. The combination of the feature extracted via PARAFAC model and the AZT procedure would therefore be a promising technique for fast zero-training online seizure detection.

Paper Nr: 28
Title:

Robotic Assisted Hand for Learning a Timing-based Task by the Elderly - Preliminary Results

Authors:

Amy E. Bouchard and Marie-Helene Milot

Abstract: With age, the timing of movement is impaired and can have a detrimental impact on functional performance. To improve motor learning, two robotic trainings, haptic guidance (HG) and error amplification (EA), could help. Some studies have found that learning is greater when demonstrating the correct movement (HG), while other studies have shown that enhancing performance error (EA) drives faster learning. Up to now, no studies have evaluated and compared HG and EA for improving the timing of movement with age. Eleven healthy seniors (mean age 68+/- 4 years) learned to play a computerized pinball-like game with their right hand, where they had to hit as many targets as possible by triggering a wrist flexion at the proper timing. This flexion caused the robot to fire, rotating the flipper, and causing the falling ball to hit a randomly positioned target. HG and EA were administered randomly. Before and after each training, a baseline and a retention condition were given and their respective timing errors were analyzed. The preliminary results showed that subjects tended to improve their timing error following HG only (p=0.09). It is possible that EA training may have been too challenging for the motor system, preventing learning.

Paper Nr: 32
Title:

Empirical Bayesian Models of L1/L2 Mixed-norm Constraints

Authors:

Deirel Paz-Linares, Mayrim Vega-Hernández and Eduardo Martínez Montes

Abstract: Inverse problems are common in neuroscience and neurotechnology, where usually a small amount of data is available with respect to the large number of parameters needed formodelling the brain activity. Classical examples are the EEG/MEG source localization and the estimation of effective brain connectivity. Many kinds of constraints or prior information have been proposed to regularize these inverse problems. Combination of smoothness (L2 norm-based penalties) and sparseness (L1 norm-based penalties) seem to be a promising approach due to its flexibility, but the estimation of optimal weights for balancing these constraints became a critical issue (Vega-Hernández et al., 2008). Two important examples of constraints that combine L1/L2 norms are the Elastic Net (Vega-Hernández et al., 2008) and the Mixed-Norm L12 (MxN, Gramfort et al., 2012). The latter imposes the properties along different dimensions of a matrix inverse problem. In this work, we formulate an empirical Bayesian model based onan MxN prior distribution. The objective is to pursuesparse learning along the first dimension (along rows) preserving smoothness in the second dimension (along columns), by estimating both parameter and hyperparameters (regularization weights).