Dry EEG Electrodes

Electroencephalography (EEG) emerged in the second decade of the 20th century as a technique for recording the neurophysiological response. Since then, there has been little variation in the physical principles that sustain the signal acquisition probes, otherwise called electrodes. Currently, new advances in technology have brought new unexpected fields of applications apart from the clinical, for which new aspects such as usability and gel-free operation are first order priorities. Thanks to new advances in materials and integrated electronic systems technologies, a new generation of dry electrodes has been developed to fulfill the need. In this manuscript, we review current approaches to develop dry EEG electrodes for clinical and other applications, including information about measurement methods and evaluation reports. We conclude that, although a broad and non-homogeneous diversity of approaches has been evaluated without a consensus in procedures and methodology, their performances are not far from those obtained with wet electrodes, which are considered the gold standard, thus enabling the former to be a useful tool in a variety of novel applications.This work was supported by Nicolo Association for the R+D+i in Neurotechnologies for disability, the research project P11-TIC-7983, Junta of Andalucia (Spain) and the Spanish National Grant TIN2012-32030, co-financed by the European Regional Development Fund (ERDF). We also thank Erik Jung, head of the Medical Microsystems working group, at the Department of System Integration & Interconnection Technologies, Fraunhofer IZM (Berlin), for his support

Wireless Sensors for Brain Activity—A Survey

Over the last decade, the area of electroencephalography (EEG) witnessed a progressive move from high-end large measurement devices, relying on accurate construction and providing high sensitivity, to miniature hardware, more specifically wireless wearable EEG devices. While accurate, traditional EEG systems need a complex structure and long periods of application time, unwittingly causing discomfort and distress on the users. Given their size and price, aside from their lower sensitivity and narrower spectrum band(s), wearable EEG devices may be used regularly by individuals for continuous collection of user data from non-medical environments. This allows their usage for diverse, nontraditional, non-medical applications, including cognition, BCI, education, and gaming. Given the reduced need for standardization or accuracy, the area remains a rather incipient one, mostly driven by the emergence of new devices that represent the critical link of the innovation chain. In this context, the aim of this study is to provide a holistic assessment of the consumer-grade EEG devices for cognition, BCI, education, and gaming, based on the existing products, the success of their underlying technologies, as benchmarked by the undertaken studies, and their integration with current applications across the four areas. Beyond establishing a reference point, this review also provides the critical and necessary systematic guidance for non-medical EEG research and development efforts at the start of their investigation.</jats:p

Έλεγχος ρομποτικού μηχανισμού μέσω εγκεφαλικών σημάτων με χρήση πλατφόρμας ανοικτού υλικού

Ο εγκέφαλος είναι για πολλούς το σημαντικότερο όργανο του ανθρώπου. Είναι υπεύθυνος για τον έλεγχο των συναισθημάτων, των κινήσεών μας και γενικότερα της συμπεριφοράς κάθε ατόμου. Για διάφορες αιτίες όμως, προκαλούνται δυσλειτουργίες, με συνέπεια την απώλεια ελέγχου της λειτουργίας των υπολοίπων δομών του σώματος. Η εισαγωγή νέων τεχνολογιών στον τομέα των νευροεπιστημών, έχει οδηγήσει τους επιστήμονες στην ανάπτυξη συστημάτων τα οποία κάνουν χρήση των εγκεφαλικών σημάτων για τον έλεγχο διάφορων συσκευών, παρακάμπτοντας το υπόλοιπο νευρικό σύστημα. Τα συστήματα αυτά είναι γνωστά με τον όρο Brain Computer Interface, B.C.I.Σκοπός της παρούσας διπλωματικής εργασίας είναι η ανάπτυξη συστήματος B.C.I, που θα ελέγχει την κίνηση ρομποτικού οχήματος μέσω εγκεφαλικών σημάτων, έχοντας ως βάση τη ρομποτική πλατφόρμα Arduino. Η λήψη του εγκεφαλικού σήματος γίνεται μέσω του Mindwave Mobile και τα δεδομένα μεταφέρονται στο Arduino ασύρματα μέσω Bluetooth. Εν συνεχεία, εξάγονται οι τιμές των επιπέδων προσοχής και συγκέντρωσης, των εγκεφαλικών ρυθμών και των ακατέργαστων δεδομένων. Γίνεται ανίχνευση του βλεφαρισμού των ματιών και αναγνώριση αν είναι απλός ή διπλός. Όταν ο βλεφαρισμός είναι απλός, το όχημα κινείται προς τα εμπρός, ενώ όταν είναι διπλός κινείται προς τα πίσω. Ταυτόχρονα, ελέγχεται η απόσταση του αμαξιδίου από πιθανά εμπόδια, οπότε και σταματάει η κίνηση του οχήματος.Human brain is the most important organ of the body. It is responsible for controlling the emotions, the movements and the behavior of the individual. For different reasons, however, human brain disabilities can result in the loss of control of the operation of the remaining structures of the body.The use of new technologies in the field of neuroscience, has led scientists to develop systems which make use of brain signals to control various devices bypassing the disabled part of nervous system These systems are known as Brain Computer Interface (B.C.I).This thesis aims to develop a real-time B.C.I system, which controls a robotic vehicle using brain signals, based on a robotic platform and specifically the Arduino. The reception of brain signals is made by using Mindwave Mobile and the data are transferred to Arduino through Bluetooth protocol. Thereafter, we export the values of the levels of attention and concentration, of brain rhythms and raw data. Then, we detect eye blinking and when it is simple or double. If it is simple, the vehicle moves forward and if it is double, it moves backwards. Simultaneously, the distance from possible obstacles is checked and in that case the vehicle stops moving

An Attention-Controlled Hand Exoskeleton for the Rehabilitation of Finger Extension and Flexion Using a Rigid-Soft Combined Mechanism

Hand rehabilitation exoskeletons are in need of improving key features such as simplicity, compactness, bi-directional actuation, low cost, portability, safe human-robotic interaction, and intuitive control. This article presents a brain-controlled hand exoskeleton based on a multi-segment mechanism driven by a steel spring. Active rehabilitation training is realized using a threshold of the attention value measured by an electroencephalography (EEG) sensor as a brain-controlled switch for the hand exoskeleton. We present a prototype implementation of this rigid-soft combined multi-segment mechanism with active training and provide a preliminary evaluation. The experimental results showed that the proposed mechanism could generate enough range of motion with a single input by distributing an actuated linear motion into the rotational motions of finger joints during finger flexion/extension. The average attention value in the experiment of concentration with visual guidance was significantly higher than that in the experiment without visual guidance. The feasibility of the attention-based control with visual guidance was proven with an overall exoskeleton actuation success rate of 95.54% (14 human subjects). In the exoskeleton actuation experiment using the general threshold, it performed just as good as using the customized thresholds; therefore, a general threshold of the attention value can be set for a certain group of users in hand exoskeleton activation

Low-cost methodologies and devices applied to measure, model and self-regulate emotions for Human-Computer Interaction

En aquesta tesi s'exploren les diferents metodologies d'anàlisi de l'experiència UX des d'una visió centrada en usuari. Aquestes metodologies clàssiques i fonamentades només permeten extreure dades cognitives, és a dir les dades que l'usuari és capaç de comunicar de manera conscient. L'objectiu de la tesi és proposar un model basat en l'extracció de dades biomètriques per complementar amb dades emotives (i formals) la informació cognitiva abans esmentada. Aquesta tesi no és només teòrica, ja que juntament amb el model proposat (i la seva evolució) es mostren les diferents proves, validacions i investigacions en què s'han aplicat, sovint en conjunt amb grups de recerca d'altres àrees amb èxit.En esta tesis se exploran las diferentes metodologías de análisis de la experiencia UX desde una visión centrada en usuario. Estas metodologías clásicas y fundamentadas solamente permiten extraer datos cognitivos, es decir los datos que el usuario es capaz de comunicar de manera consciente. El objetivo de la tesis es proponer un modelo basado en la extracción de datos biométricos para complementar con datos emotivos (y formales) la información cognitiva antes mencionada. Esta tesis no es solamente teórica, ya que junto con el modelo propuesto (y su evolución) se muestran las diferentes pruebas, validaciones e investigaciones en la que se han aplicado, a menudo en conjunto con grupos de investigación de otras áreas con éxito.In this thesis, the different methodologies for analyzing the UX experience are explored from a user-centered perspective. These classical and well-founded methodologies only allow the extraction of cognitive data, that is, the data that the user is capable of consciously communicating. The objective of this thesis is to propose a methodology that uses the extraction of biometric data to complement the aforementioned cognitive information with emotional (and formal) data. This thesis is not only theoretical, since the proposed model (and its evolution) is complemented with the different tests, validations and investigations in which they have been applied, often in conjunction with research groups from other areas with success

Proceedings of the 19th Sound and Music Computing Conference

Proceedings of the 19th Sound and Music Computing Conference - June 5-12, 2022 - Saint-Étienne (France). https://smc22.grame.f