Breaking fresh ground in human–media interaction research

Human-Media Interaction research is devoted to methods and situations where humans individually or collectively interact with digital media, systems, devices and environments. Novel forms of interaction paradigms have been enabled by new sensor and actuator technology in the last decades, combining with advances in our knowledge of human-human interaction and human behavior in general when designing user interfaces

Optimal pseudorandom sequence selection for online c-VEP based BCI control applications

<div><p>Background</p><p>In a c-VEP BCI setting, test subjects can have highly varying performances when different pseudorandom sequences are applied as stimulus, and ideally, multiple codes should be supported. On the other hand, repeating the experiment with many different pseudorandom sequences is a laborious process.</p><p>Aims</p><p>This study aimed to suggest an efficient method for choosing the optimal stimulus sequence based on a fast test and simple measures to increase the performance and minimize the time consumption for research trials.</p><p>Methods</p><p>A total of 21 healthy subjects were included in an online wheelchair control task and completed the same task using stimuli based on the m-code, the gold-code, and the Barker-code. Correct/incorrect identification and time consumption were obtained for each identification. Subject-specific templates were characterized and used in a forward-step first-order model to predict the chance of completion and accuracy score.</p><p>Results</p><p>No specific pseudorandom sequence showed superior accuracy on the group basis. When isolating the individual performances with the highest accuracy, time consumption per identification was not significantly increased. The Accuracy Score aids in predicting what pseudorandom sequence will lead to the best performance using only the templates. The Accuracy Score was higher when the template resembled a delta function the most and when repeated templates were consistent. For completion prediction, only the shape of the template was a significant predictor.</p><p>Conclusions</p><p>The simple and fast method presented in this study as the Accuracy Score, allows c-VEP based BCI systems to support multiple pseudorandom sequences without increase in trial length. This allows for more personalized BCI systems with better performance to be tested without increased costs.</p></div

Smart Room System for Paralysis Patients with Mindwave EEG Sensor Control

Persons with disabilities experience physical, intellectual, mental, or sensory difficulties. One type of disability is paralysis. Paralysis is a condition where there is interference with the nerves that control body movement, causing the limbs to be unable to move. Paralyzed people will find it difficult to move without the help of others. Therefore, research was carried out by creating an intelligent room system to help persons with disabilities manage their own rooms so that they do not always have to be accompanied by a nurse. Paralyzed people can turn lights or fans on and off, and send help messages to their carers via the Telegram bot. This study used the NeuroSky Mindwave EEG headset which detects the user's brain signals with outputs in the form of attention level, relaxation level (meditation), and blink strength level. The resulting signal is processed via a PC and sent via NodeMCU to give commands in the form of turning lights and fans on or off, as well as sending messages to nurses. From this research a system was produced that could turn on the lights based on the value of Attention ≥ 70, turn on the fan based on the Meditation value ≥ 74, then the value of BlinkStrength ≥ 81 which was counted 2 times to turn off the lights, 3 times to turn off the fan, 4 times to turn off the lights and fan, and more than 4 times sending help message

Perception and cognition of cues Used in synchronous Brain–computer interfaces Modify electroencephalographic Patterns of control Tasks

A motor imagery (MI)-based brain–computer interface (BCI) is a system that enables humans to interact with their environment by translating their brain signals into control commands for a target device. In particular, synchronous BCI systems make use of cues to trigger the motor activity of interest. So far, it has been shown that electroencephalographic (EEG) patterns before and after cue onset can reveal the user cognitive state and enhance the discrimination of MI-related control tasks. However, there has been no detailed investigation of the nature of those EEG patterns. We, therefore, propose to study the cue effects on MI-related control tasks by selecting EEG patterns that best discriminate such control tasks, and analyzing where those patterns are coming from. The study was carried out using two methods: standard and all-embracing. The standard method was based on sources (recording sites, frequency bands, and time windows), where the modulation of EEG signals due to motor activity is typically detected. The all-embracing method included a wider variety of sources, where not only motor activity is reflected. The findings of this study showed that the classification accuracy (CA) of MI-related control tasks did not depend on the type of cue in use. However, EEG patterns that best differentiated those control tasks emerged from sources well defined by the perception and cognition of the cue in use. An implication of this study is the possibility of obtaining different control commands that could be detected with the same accuracy. Since different cues trigger control tasks that yield similar CAs, and those control tasks produce EEG patterns differentiated by the cue nature, this leads to accelerate the brain–computer communication by having a wider variety of detectable control commands. This is an important issue for Neuroergonomics research because neural activity could not only be used to monitor the human mental state as is typically done, but this activity might be also employed to control the system of interest

EEG-pohjaisten BCI-laitteiden käyttö kommunikoinnin apuvälineenä

Aivojen vaurio tai sairaus voi aiheuttaa vaikeuksia kommunikoinnissa niin, ettei kommunikointi perinteisellä tavalla eli puheella enää onnistu. Vaurion tai sairauden seurauksena puhekykynsä menettäneet henkilöt käyttävät puhetta korvaavia kommunikointikeinoja, joita perinteisesti ovat olleet erilaiset manuaaliset kommunikointikansiot ja -kommunikointitaulut. Nämä perinteiset menetelmät kuitenkin vaativat usein käyttäjää kommunikoimaan avustajan avulla. Teknologian kehittyminen mahdollistaa toimintakykynsä menettäneelle henkilölle itsenäisemmän kommunikoinnin. EEG-pohjaiset ohjausmenetelmät ovat eräs tulevaisuuden kommunikoinnin apuvälineiden kehityssuunnista. Sellaisia laitteita, joita ohjataan aivoista tulevien signaalien avulla, kutsutaan BCI-laitteiksi. Tämän tutkielman tutkimuskysymyksenä oli selvittää, onko nykyisellä teknologialla mahdollista toteuttaa toimivia EEG-pohjaisia BCI-laitteita hyödyntäviä sovelluksia kuluttajilla suunnitelluilla BCI-laiteilla. Tässä tutkielmassa selvitetään EEG-pohjaisten BCI-laitteiden soveltuvuutta kommunikoinnin apuvälineiden ohjaamiseen BCIController-ohjelman avulla. BCIController tulkitsee käyttäjän aivosähkötoiminnasta keskittymistason ja liikuttaa sen perusteella ruudulla näkyvää pistettä. Järjestelmän toimivuutta testattiin koehenkilöiden avulla. Tehdyssä tutkimuksessa EEG-pannan avulla saatiin lupaavia tuloksia. Se saattaisi soveltua tulevaisuudessa kommunikoinnin apuvälineeksi, kunhan sen käytössä ilmeneviä ongelmia saadaan ensin ratkottua. Ongelmia olivat muun muassa sensorien lukutarkkuuden ongelmat sekä vaadittavan keskittymistason ylläpitämisen haasteellisuus. Kommunikoinnin apuvälineeksi sovelluttamiseksi vaaditaan niin käyttäjäkokemuksien huomioon ottamista kuin myös moniammatillista yhteistyötä IT-asiantuntijoiden ja toimintakykyä kuntouttavien asiantuntijoiden välillä

Quantum Brain Networks: A Perspective

We propose Quantum Brain Networks (QBraiNs) as a new interdisciplinary field integrating knowledge and methods from neurotechnology, artificial intelligence, and quantum computing. The objective is to develop an enhanced connectivity between the human brain and quantum computers for a variety of disruptive applications. We foresee the emergence of hybrid classical-quantum networks of wetware and hardware nodes, mediated by machine learning techniques and brain– machine interfaces. QBraiNs will harness and transform in unprecedented ways arts, science, technologies, and entrepreneurship, in particular activities related to medicine, Internet of Humans, intelligent devices, sensorial experience, gaming, Internet of Things, crypto trading, and business

DIY hybrid SSVEP-P300 LED stimuli for BCI platform using EMOTIV EEG headset

A fully customisable chip-on board (COB) LED design to evoke two brain responses simultaneously (steady state visual evoked potential (SSVEP) and transient evoked potential, P300) is discussed in this paper. Considering different possible modalities in brain-computer interfacing (BCI), SSVEP is widely accepted as it requires a lesser number of electroencephalogram (EEG) electrodes and minimal training time. The aim of this work was to produce a hybrid BCI hardware platform to evoke SSVEP and P300 precisely with reduced fatigue and improved classification performance. The system comprises of four independent radial green visual stimuli controlled individually by a 32-bit microcontroller platform to evoke SSVEP and four red LEDs flashing at random intervals to generate P300 events. The system can also record the P300 event timestamps that can be used in classification, to improve the accuracy and reliability. The hybrid stimulus was tested for real-time classification accuracy by controlling a LEGO robot to move in four directions