NEUROTECHNIX 2018 Abstracts


Area 1 - Neural Rehabilitation and Neuroprosthetics

Full Papers
Paper Nr: 19
Title:

Data-efficient Motor Imagery Decoding in Real-time for the Cybathlon Brain-Computer Interface Race

Authors:

Eduardo G. Ponferrada, Anastasia Sylaidi and A. Aldo Faisal

Abstract: Neuromotor diseases such as Amyotrophic Lateral Sclerosis or Multiple Sclerosis affect millions of people throughout the globe by obstructing body movement and thereby any instrumental interaction with the world. Brain Computer Interfaces (BCIs) hold the premise of re-routing signals around the damaged parts of the nervous system to restore control. However, the field still faces open challenges in training and practical implementation for real-time usage which hampers its impact on patients. The Cybathlon Brain-Computer Interface Race promotes the development of practical BCIs to facilitate clinical adoption. In this work we present a competitive and data-efficient BCI system to control the Cybathlon video game using motor imageries. The platform achieves substantial performance while requiring a relatively small amount of training data, thereby accelerating the training phase. We employ a static band-pass filter and Common Spatial Patterns learnt using supervised machine learning techniques to enable the discrimination between different motor imageries. Log-variance features are extracted from the spatio-temporally filtered EEG signals to fit a Logistic Regression classifier, obtaining satisfying levels of decoding accuracy. The systems performance is evaluated online, on the first version of the Cybathlon Brain Runners game, controlling 3 commands with up to 60.03% accuracy using a two-step hierarchical classifier.

Short Papers
Paper Nr: 2
Title:

An Optogenetic Platform for Freely Moving Animal Applications

Authors:

Dimitris Firfilionis, JunWen Luo and Patrick Degenaar

Abstract: In this paper, we present an optogenetic platform with simultaneous electrical recording and optical stimulation, combined with closed-loop processing capabilities. The neural interface Application Specific Integrated Circuit (ASIC) placed on the head-stage provides four low noise (1.15 μVrms) recording channels, targeting Local Field Potential (LFP) recording. For stimulation it provides six independently addressable optical driver circuits, which offer both intensity and pulse-width modulation for high radiance LEDs. The ASIC also includes a fully-digital Serial Peripheral Interface (SPI) embedded within a Finite State Machine (FSM). This allows the ASIC to be controlled by external embedded controllers. The low power microcontroller used in this system is part of the Kinetis K22F sub-family (ARM Cortex M4), and is responsible for controlling the ASIC and storing the recorded data in a 1GB μSD card. Apart from the neural interface ASIC, which was implemented on a 0.35 μm CMOS technology, the head-stage and embedded control units were built using off-the-self components and rigid PCBs. The embedded control unit PCB occupies a space of 25x22 mm, while the head-stage has a size of 10x10 mm. The two units are connected via a 10-way Flat Flexible Cable (FFC), which provides power and clock signals, and bidirectional communication though the SPI interface.

Area 2 - Neuroimaging and Neurosensing

Short Papers
Paper Nr: 3
Title:

A Robust Method for the Individual Alpha Frequency Detection in EEG

Authors:

F. Grosselin, Y. Attal and M. Chavez

Abstract: We present a method to determine the individual alpha (α) peak frequency (IAF) of EEG segments. The algorithm uses information over previous time-windows to determine the current IAF. First, the 1/ f trend of the spectrum is estimated by an iterative curve-fitting procedure and then removed from the spectrum. Finally, local maxima are identified in the corrected spectrum. If an α peak is ambiguous, i.e. when several peaks are observed due to different physiological α activations or to a noisy spectral activity, the algorithm selects the most probable one based on the peaks detected in previous time windows. This approach allows the detection of small α activities and ensures a precise and stable detection of the α peak, without offline analysis or a prior estimation of a reference spectrum. This is particularly important for real-time applications like α-based neurofeedback for which a precise and stable feedback is required for an efficient learning.

Paper Nr: 4
Title:

Variation of the EEG-Energy in a Second Language Class

Authors:

Freddy L. Bueno-Palomeque, Efrén L. Lema-Condo, Susana E. Castro-Villalobos, Luis J. Serpa-Andrade and Esteban F. Ordoñez-Morales

Abstract: The attention, concentration, and anxiety of the students are fundamental in the learning process of a second language. This study proposes to register EEG signals during a second language class to quantify the energy level of the EEG signal and associate it with the attention level along the time. Data was registered while attending to a level 1 English language through 12 minutes. The signals were filtered using a wavelet transform Symlet 6 to analyze two frequency bands: Alpha (8-16 Hz) and Beta (16-32 Hz). The results revealed an increment in the energy on the electrodes AF3, AF4, F3, and F4 in the Alpha and Beta bands between 37.25 and 43.41% of the students, and a decrement between 25.49 and 43.13%. Finally, when analyzing the electrodes T7 y T8, there were an increment of the energy in 35.30% of the students and decrements between 39.22 and 47.60% of theirs.

Paper Nr: 5
Title:

A Preliminary System for the Automatic Detection of Emotions based on the Autonomic Nervous System Response

Authors:

Asier Salazar-Ramirez, Raquel Martinez, Andoni Arruti, Eloy Irigoyen, J. Ignacio Martin and Javier Muguerza

Abstract: People’s life quality is being improved thanks to the advances in medicine and to the promotion of health. One of the pillars for having a healthy life is to know and to take care of the emotions of oneself. Due to the close relationship between the emotions and the responses of the autonomic nervous system, the aim of this work is to study and detect the physiological patterns produced by two of the basic human emotions: surprise and contentment. The work presents a preliminary system that processes and analyzes two non-invasive physiological signals (the galvanic skin response and the heart rate variability) and that uses a finite state machine for the detection of the activation of the sympathetic nervous system. The work also presents the experimental procedure that was designed in order to elicit different emotions in laboratory conditions. The F-score results obtained for the correlation of the analyzed emotions and the physiological patterns were F1=1.00 and for surprise and F1=0.94 for contentment.

Paper Nr: 7
Title:

Method of Acute Alertness Level Evaluation after Exposure to Blue and Red Light (based on EEG): Technical Aspects

Authors:

Agnieszka Wolska, Dariusz Sawicki, Kamila Nowak, Mariusz Wisełka and Marcin Kołodziej

Abstract: Since maintaining a high level of alertness is a very important factor on many workstations a number of studies on alertness level evaluation have been carrying out. The effects of light exposure on alertness level have been the subject of many studies with EEG registration, but technical aspects of planning experiment are not well described. The aim of the article is to present the evaluation method of acute alertness level after exposure to blue and red light based on EEG registration with special attention to technical aspects of used methodology. The preliminary results obtained during the pilot study confirmed that elaborated method fulfils our expectations and gives opportunity to assess the acute alertness after exposure to light.

Area 3 - Neuroinformatics and Neurocomputing

Short Papers
Paper Nr: 13
Title:

The Role of Robot Design in Decoding Error-related Information from EEG Signals of a Human Observer

Authors:

Joos Behncke, Robin T. Schirrmeister, Wolfram Burgard and Tonio Ball

Abstract: For utilization of robotic assistive devices in everyday life, means for detection and processing of erroneous robot actions are a focal aspect in the development of collaborative systems, especially when controlled via brain signals. Though, the variety of possible scenarios and the diversity of used robotic systems pose a challenge for error decoding from recordings of brain signals such as via EEG. For example, it is unclear whether humanoid appearances of robotic assistants have an influence on the performance. In this paper, we designed a study in which two different robots executed the same task both in an erroneous and a correct manner. We find error-related EEG signals of human observers indicating that the performance of the error decoding was independent of robot design. However, we can show that it was possible to identify which robot performed the instructed task by means of the EEG signals. In this case, deep convolutional neural networks (deep ConvNets) could reach significantly higher accuracies than both regularized Linear Discriminanat Analysis (rLDA) and filter bank common spatial patterns (FB-CSP) combined with rLDA. Our findings indicate that decoding information about robot action success from the EEG, particularly when using deep neural networks, may be an applicable approach for a broad range of robot designs.

Area 4 - Neuromodulation and Neural Engineering

Full Papers
Paper Nr: 8
Title:

Real-time Phase Detection for EEG-based tACS Closed-loop System

Authors:

G. Zarubin, C. Gundlach, V. Nikulin and M. Bogdan

Abstract: In this paper, we present a robust and fast implementation of a closed loop EEG-transcranial-alternating-current-stimulation (tACS) paradigm focusing on phase coupling between the tACS signal and alpha-oscillations of the ongoing EEG signal. We provide an evaluation of three phase-prediction methods for alpha oscillations of offline EEG data and for artificially generated oscillations with different noise levels in terms of optimization time as well as accuracy of prediction. Successful functioning of the whole system with delays compensation and data corrections is demonstrated in real-time pilot measurements with humans.

Short Papers
Paper Nr: 10
Title:

Resorbable PLGA Microneedles to Insert Ultra-fine Electrode Arrays in Neural Tissue for Chronic Recording

Authors:

Frederik Ceyssens, Marta Bovet Carmona, Dries Kil, Marjolijn Deprez, Bart Nuttin, Aya Takeoka, Detlef Balschun and Robert Puers

Abstract: Recent work has indicated that it is possible to keep artificial structures such as neural electrode arrays in close contact with neural tissue over chronic timescales, without the formation of scar tissue. The main factor allowing this is a compliance of the structures close to that of the tissue it is embedded in. However, as this results in structures that are too weak to be inserted as such, the question comes up which insertion strategy to use. In this work, we investigate the use of polylactic-co-glycolic acid (PLGA) for this purpose. A process was devised that allows to micromachine needle-shaped PLGA structures, and to embed ultra fine electrode arrays in the needle. The electrode arrays are fabricated using thin-film polyimide technology. They are only 1 micrometer thick, and contain 15 micrometer diameter iridium oxide electrodes aimed at single neuron recording. The implants were tested in vivo over chronic timescales in rats, and were evaluated based on evoked potential and action potential recording as well as post mortem histology (GFAP and NeuN stain). It was concluded that scarring was minimal but still present, with a GFAP scar about 5x smaller in area than the cross section of the PLGA needle itself. The electric recordigs are stable for at least the 4-month duration of the experiment.

Paper Nr: 15
Title:

Wireless Thermal Neuromodulator for Long-term in Vivo Cooling Performance Assessment

Authors:

A. M. Miranda, C. Silva, V. Silva and P. M. Mendes

Abstract: Focal cooling is considered a potential solution to stop or control epileptic activity. However, despite the available proof of concept studies, such approach requires further validation before being used in humans. One hindering factor is the lack of suitable devices to enable large-scale validation of such methodology. This paper presents a wireless thermal neuromodulator that can wirelessly record the rat’s brain electrical activity and temperature. At the same time, the temperature is reduced without the need to use cumbersome liquid pipes. The proposed device has two modules: one headstage with a cooler and sensors, and one backpack with acquisition electronics and wireless communication capability. It is possible to record the brain temperature, the EEG at 16 kbps, and to control the cooler’s temperature, with an autonomy of 1 day.