Decoding Finger Flexion from Band-Specific ECoG Signals in Humans

This article presents the method that won the brain-computer interface (BCI) competition IV addressed to the prediction of the finger flexion from electrocorticogram (ECoG) signals. ECoG-based BCIs have recently drawn the attention from the community. Indeed, ECoG can provide higher spatial resolution and better signal quality than classical EEG recordings. It is also more suitable for long-term use. These characteristics allow to decode precise brain activities and to realize efficient ECoG-based neuroprostheses. Signal processing is a very important task in BCIs research for translating brain signals into commands. Here, we present a linear regression method based on the amplitude modulation of band-specific ECoG including a short-term memory for individual finger flexion prediction. The effectiveness of the method was proven by achieving the highest value of correlation coefficient between the predicted and recorded finger flexion values on data set 4 during the BCI competition IV

Détection et traitement de "données à problèmes"

Colloque avec actes et comité de lecture.Les performances des systèmes se résument bien souvent à une mesure statistique moyenne. Mais l'erreur commise peut varier fortement en fonction de la forme présentée. Ainsi, quelques situations difficiles à traiter peuvent dégrader fortement les performances. Partant de la distribution normale des erreurs généralement produite par les réseaux neuromimétiques, une détection et un traitement particulier sont appliqués aux données qui génèrent les plus fortes erreurs dans un problème de régression afin d'améliorer les performances

Practical introduction to artificial neural networks

Colloque avec actes et comité de lecture. nationale.National audienceWhat are they ? What for are they ? How to use them ? This article wants to answer these three fundamental questions about artificial neural networks that every engineer interested by this machine learning technique asks to oneself. We present the most useful architectures. We explain how to train them using a supervised or an unsupervised learning depending on the task we want to do : regression, discrimination or clustering. What kind of data can one use and how to prepare them ? Finally, we will be interested to which confidence can we give to the observed results

Inverse reinforcement learning to control a robotic arm using a Brain-Computer Interface

The goal of this project is to use inverse reinforce- ment learning to better control a JACO robotic arm developed by Kinova in a Brain-Computer Interface (BCI). A self-paced BCI such as a motor imagery based-BCI allows the subject to give orders at any time to freely control a device. But using this paradigm, even after a long training, the accuracy of the classifier used to recognize the order is not 100%. While a lot of studies try to improve the accuracy using a preprocessing stage that improves the feature extraction, we work on a post- processing solution. The classifier used to recognize the mental commands will provide as outputs a value for each command such as the posterior probability. But the executed action will not only depend on this information. A decision process will also take into account the position of the robotic arm and previous trajectories. More precisely, the decision process will be obtained applying an inverse reinforcement learning (IRL) on a subset of trajectories specified by an expert. At the end of the workshop, the convergence of the inverse reinforcement algorithm has not been achieved. Nevertheless, we developed a whole processing chain based on OpenViBE for controlling 2D- movements and we present how to deal with this high dimensional time series problem with a lot of noise which is unusual for the IRL community

Template-based classifiers for ERP-based BCIs

International audienceThis talk aim to present pattern recognition techniques of graphic elements (e.g. event-related potential, auditory evoked potential, k-complex, sleep spindles, vertex waves) included in electro-encephalographic signals. More specifically, template-based classifiers will be introduced to robustly detect evoked potentials in a single trial from noisy and multi-sources electro-encephalographic signals

Averaging techniques for single-trial analysis of oddball event-related potentials

International audienceMore and more effort is done in BCI research to improve its usability for patients, with respect to its communication speed and transmission accuracy. In this contribution, we ex- periment with BCI speller based on P300 evoked potential. More precisely, the typical form of event-related potential (ERP) inspires us to devise classification methods based on the simi- larity/dissimilarity in the time domain between single trials and one or several estimated ERP templates derived from sub ject recordings. The reliable estimation of template is difficult in a single trial due to the low signal-to-noise ratio (SNR) of electroencephalographic (EEG) signals. We first explicitly estimate the template using several averaging techniques: point- to-point averaging, cross-correlation alignment and dynamic time warping. Then we inexplic- itly estimate several ERP templates using learning vector quantization algorithm combined with an extreme learning machine. Finally classification is realized based on the similar- ity/dissimilarity between the single trials and the template. Simulation is carried out using a BCI competition III data set acquired with the P300 speller paradigm. The experiments show that template-based classifiers can also obtain high accuracy

Wavelet denoising for P300 single-trial detection

National audienceTemplate-based analysis techniques are good candidates to robustly detect transient temporal graphic elements (e.g. event-related potential, k-complex, sleep spindles, vertex waves, spikes) in noisy and multi-sources electro-encephalographic signals. More specifically, we present the impact on a large dataset of a wavelet denoising to detect evoked potentials in a single-trial P300 speller. Using coiflets as a denoising process allows to obtain more stable accurracies for all subjects

HUBUNGAN TINGKAT PENDIDIKAN KEPALA KELUARGA DENGAN TINGKAT KESIAPSIAGAAN BENCANA TSUNAMI DI DESA ALUE NAGA KECAMATAN SYIAH KUALA KOTA BANDA ACEH

ABSTRAKTsunami pada Desember 2004, yang dipicu oleh gempa berkekuatan 9.0 SR di sebelah utara pulau Sumatra mengakibatkan kerugian yang sangat besar. Aceh merupakan daerah paling parah dengan korban tewas sebanyak 123.000 jiwa, 113.000 orang hilang, 406.000 orang kehilangan tempat tinggal. Faktor utama timbulnya banyak korban akibat bencana gempa bumi adalah kurangnya pengetahuan masyarakat tentang bencana dan kesiapan mereka dalam mengantisipasi bencana. Sekolah merupakan salah satu media transformasi ilmu pengetahuan yang paling efektif dalam menyerap dan mengaplikasikan pengetahuan kesiapan menghadapi bencana dengan menggunakan metode yang tepat dan benar. Tujuan penelitian ini adalah untuk mengetahui apakah ada hubungan tingkat pendidikan formal dengan tingkat kesiapsiagaan dalam menghadapi bencana tsunami. Populasi penelitian ini 494 orang kepala keluarga dan terdapat jumlah sampel sebanyak 84 responden. Pengambilan sampel yang digunakan adalah non-probability sampling. Pengumpulan data menggunakan angket dengan wawancara terarah dan diolah dengan menggunakan statistik uji Kolmogorov Smirnov dengan hasil 55 responden (66%) memiliki kesiapsiagaaan rendah dan tingkat pendidikan yang paling banyak adalah pendidikan dasar berjumlah 67 responden (80%). Kesimpulan dalam penelitian ini adalah terdapat pengaruh tingkat pendidikan dengan tingkat kesiapsiagaan kepala keluaga di Desa Alue Naga kecamatan Syiah Kuala Kota Banda Aceh dengan nilai p=0,000 (pBanda Ace

Wavelet-based Semblance for P300 Single-trial Detection

International audienceElectroencephalographic signals are usually contaminated by noise and artifacts making difficult to detect Event-Related Potential (ERP), specially in single trials. Wavelet denoising has been successfully applied to ERP detection, but usually works using channels information independently. This paper presents a new adaptive approach to denoise signals taking into account channels correlation in the wavelet domain. Moreover, we combine phase and amplitude information in the wavelet domain to automatically select a temporal window which increases class separability. Results on a classic Brain-Computer Interface application to spell characters using P300 detection show that our algorithm has a better accuracy with respect to the VisuShrink wavelet technique and XDAWN algorithm among 22 healthy subjects, and a better regularity than XDAWN