BCI Research Papers - Academia.edu (original) (raw)

- by
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- Non-Invasive BCI, Medicine, BCI, EEG Signal Processing
- by Bruce A Lawrence
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- Nursing, BCI, Humans, Injury
- by Eric Pohlmeyer
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- Computer Science, Artificial Intelligence, Computer Vision, Biomedical Engineering
- by Rubem Santos
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- BCI

This paper presents an implementation of an ef- fective synchronization between a registration and a stimula- tion module that form part of a steady state visual evoked potential (SSVEP)-based Brain Computer Interface (BCI). Each of the synchronized modules were created and designed in two different platforms as well as in two different worksta- tions, so a particular objective of this work consisted in finding the best platforms to stimulate and register the EEG in order to create a communication channel and protocol between the two different modules. The resulting synchronization was tested with an existing SSVEP stimulation protocol in one subject. Even though the results were promising, a test on a bigger population is needed.

- by Fernando Alarid-Escudero and +1
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- EEG, Non-Invasive BCI, BCI, Steady State Visual Evoked Potentials
- by Ramakrishnan Angarai Ganesan
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- Computer Science, Rehabilitation, EEG, Brain Computer Interface

The recent advancements in the field of embedded systems enables us to develop a reliable smart home system for easier access to home appliances. The aim is to provide a seamless integration of various methods of controlling appliances in the smart home aiding the physically challenged. This can be done by means of a non-invasive Brain-Computer Interface (BCI), text messaging and a personalized web page. This paper proposes a novel method wherein the physically challenged people can control the appliances by using Emotiv EPOC headset which reads the Electroencephalographic (EEG) signals recorded from the brain activity and communicates with an Arduino board. The proposed system has been built with the help of an Ethernet shield which runs a webserver, and a GSM shield which communicates with the Arduino through text messaging (SMS).

- by IAEME Publication
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- Arduino, BCI, Smart Home, Ethernet

In this paper designing of a battery operated portable single channel electroencephalography (EEG) signal acquisition system is presented. The advancement in the field of hardware and signal processing tools made possible the utilization of brain waves for the communication between humans and computers. The work presented in this paper can be said as a part of bigger task, whose purpose is to classify EEG signals belonging to a varied set of mental activities in a real time Brain Computer Interface (BCI). Keeping in mind the end goal is to research the possibility of utilizing diverse mental tasks as a wide correspondence channel in the middle of individuals and PCs. This work deals with EEG based BCI, intent on the designing of portable EEG signal acquisition system. The EEG signal acquisition system with a cut off frequency band of 1-100 Hz is designed by the use of integrated circuits such as low power instrumentation amplifier INA128P, high gain operational amplifiers LM358P. Initially the amplified EEG signals are digitized and transmitted to a PC by a data acquisition module NI DAQ (SCXI-1302). These transmitted signals are then viewed and stored in the LAB VIEW environment. From a varied set of experimental observation it can be said that the system can be implemented in the acquisition of EEG signals and can stores the data to a PC efficiently and the system would be of advantage to the use of EEG signal acquisition or even BCI application by adapting signal processing tools.

- by Amlan Jyoti Bhagawati and +1
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- EEG, BCI, Amplifier, Data Acquisition System

How the aesthetics of virtual reality and the promise of embodied "presence" have been re-articulated from the camera obscura and magic lantern, through the nineteenth-century's panoramic and stereoscopic formats, and in today's emergent... more

- by Brooke Belisle
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- Panoramas, Virtual Art & Virtual Reality, BCI, Virtual Reality
- by Marcin Kołodziej
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- Machine Learning, EEG, BCI

Birds are considered excellent bio-indicators and ideal models for predicting environmental changes due to the effects of urbanization on ecosystems since they are highly diverse and conspicuous biota of the ecosystem. Bird species respond rapidly to changes in landscape alteration, composition and function and to the availability of habitat structures. Birds were classified into categories based on behavioral and physiological response guilds and a Bird Community Index Score (BCI) was calculated based on the types of birds present. As habitats shift from undisturbed to degraded, there will be a corresponding shift from specialist to generalist species because disturbed habitats could not support very specialized species. So, urban and industrial areas may be a very good area for observing such kind shift among the bird species. The paper is a review is of this kind of study.

Recent advances in neuroimaging demonstrate the potential of functional near-infrared spectroscopy (fNIRS) for use in brain–computer interfaces (BCIs). fNIRS uses light in the near-infrared range to measure brain surface haemoglobin concentrations and thus determine human neural activity. Our primary goal in this study is to analyse brain haemodynamic responses for application in a BCI. Specifically, we develop an efficient signal processing algorithm to extract important mental-task-relevant neural features and ...

- by Jinung An
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- Engineering, Algorithms, Artificial Intelligence, Cognition
- by Michele Barsotti
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- Brain-computer interfaces, BCI, EEG Signal Processing, Motor Imagery

"Ludzkie zdolności poznawcze i sensualne są znacznie ograniczone. Człowiek posiada zaledwie 5 podstawowych zmysłów – wzrok, słuch, smak, węch, dotyk; oraz dodatkowe zmysły równowagi i propriocepcja. Jakby tego było mało posiadane zmysły są słabo dostrojone np. wzrok pozwala na obserwację drobnego wycinka promieniowania elektromagnetycznego (światło widzialne w zakresie długości fali 380-780 nm), z użyciem jedynie trzech fotoreceptorów widzenia barwnego i jednego odpowiedzialnego za postrzeganie monochromatyczne, dla porównania rawka błazen (łac. Odontodactylus scyllarus, potocznie krewetka modliszkowa) posiada aż 16 fotoreceptorów, przy tym widząc również w podczerwieni i ultrafiolecie. Czy świat innych zmysłów (jak zwierzęca echolokacja, elektrorecepcja, magnetorecepcja, czy znane ze parapsychologii telekineza, telepatia) jest zamknięty dla człowieka? Nauka zdaje się mieć receptę: tam gdzie człowiek nie został doposażony ewolucyjnie przez naturę z pomocą przychodzi mu technologia, która umożliwi poprawą zdolności poznawczych (ang. cognitive enhancement).
Poprawa zdolności poznawczych i zmysłów człowieka, jest znacząco akcentowana w nurcie filozofii transhumanistycznej i odnosi się do nadrzędnej koncepcji wzmocnienia ludzkiego (ang. human enhancement) – uzyskanie długowieczności, większej witalności, ogólnego dobrobytu i super-inteligencji. Szczególnym przypadkiem wzmocnienia ludzkiego jest wzmocnienie poznawcze/kognitywne (ang. cognitive enhancement). Zdaniem transhumanistów poprawa zdolności władz umysłowych człowieka obejmować powinna: zwiększenie pamięci, inteligencji, polepszenia myślenia abstrakcyjnego i logicznego, a także winna obejmować: rozszerzenie zasięgu ludzkich zmysłów, udoskonalenie układu nerwowego, uzupełnienie kory mózgu nowym „metamózgiem”, mogący wpływać na czucie somatyczne tym samym zwiększając samoświadomość i dając możliwość przestrajania emocji, redukcję popędów oraz wysubtelnienie niektórych stanów emocjonalnych, przekraczanie poza typowe ludzkie zmysły, czy też przekroczenie środowiska naturalnego poprzez stopniową wirtualizację życia, w której człowiek będzie żył na granicy świata realnego i wirtualnego.
Metody mające wzmacniać procesy poznawcze oraz ludzkie zmysły, można podzielić na następujące grupy: biotechnologiczne (neurofarmakologia, genetyka), informatyczne (sztuczna inteligencja, nanotechnologia, robotyka, komunikacja human-computer interaction), niefarmakologiczne (mnemotechnika, medytacja, sen, trening mentalny, odżywianie), innym podziałem jest wydzielenie metod inwazyjnych i nieinwazyjnych.
W moim wystąpieniu skupiam się analizie metod i technologii (dostępnych i teoretycznych) mających usprawnić ludzkie poznanie zmysłowe. Przedstawiam w jaki sposób dochodzi do udoskonalenia ludzkich zmysłów oraz jak możliwy jest odbiór niedostępnych zmysłów. Ponadto rozważam również, konsekwencje takich działań w kontekście etycznym i społecznym.
"

- by Artur Gunia
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- Posthumanism, Augmented Reality, Neuroethics, Transhumanism

Brain-Computer Interface (BCI) is a fast-growing emergent technology in which researchers aim to build a direct channel between the human brain and the computer. It is a collaboration in which a brain accepts and controls a mechanical device as a natural part of its representation of the body. The BCI can lead to many applications especially for disabled persons. Most of these applications are related to disable persons in which they can help them in living as normal people. Wheelchair control is one of the famous applications in this field. In addition, the BCI research aims to emulate the human brain. This would be beneficial in many fields including the Artificial Intelligence and Computational Intelligence. Throughout this chapter, an introduction to the main concepts behind the BCI is given, the concepts of the brain anatomy is explained, and the BCI different signals are stated. In addition, the used hardware and software for the BCI are elaborated.

- by Ermi Ermi and +1
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- Neuroscience, BCI
- by Marcin Kołodziej
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- BCI

The aim of this study is to control home devices
using a non invasive brain computer interface (BCI). The
Electroencephalographic signals (EEG) recorded from the brain
activity using the Emotiv EPOCH headset are interfaced with the
help of mouse emulator to a graphical user interface (GUI) on the
computer screen. The user will use this GUI to control various
devices in a smart home. This application will be very useful
especially for people with special needs.

- by humaira nisar
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- Non-Invasive BCI, BCI
- by Linh Hoang
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- Neuroscience, Signal Processing, EEG, BCI

We present the evaluation of two well-known, low-cost consumer-grade EEG devices: the Emotiv EPOC and the Neurosky MindWave. Problems with using the consumer-grade EEG devices (BCI illiteracy, poor technical characteristics, and adverse EEG artefacts) are discussed. The experimental evaluation of the devices, performed with 10 subjects asked to perform concentration/relaxation and blinking recognition tasks, is given. The results of statistical analysis show that both devices exhibit high variability and non-normality of attention and meditation data, which makes each of them difficult to use as an input to control tasks. BCI illiteracy may be a significant problem, as well as setting up of the proper environment of the experiment. The results of blinking recognition show that using the Neurosky device means recognition accuracy is less than 50%, while the Emotiv device has achieved a recognition accuracy of more than 75%; for tasks that require concentration and relaxation of subjects, the Emotiv EPOC device has performed better (as measured by the recognition accuracy) by ∼9%. Therefore, the Emotiv EPOC device may be more suitable for control tasks using the attention/meditation level or eye blinking than the Neurosky MindWave device.

- by Robertas Damasevicius and +1
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- EEG, BCI

There are approximately 21 million disabled folks in India, which is equivalent to 2.2% of the total population. These disabled individuals are impacted by numerous neuromuscular disorders. To enable them to express themselves, one can supply them with alternative and augmentative communication. For this, a Brain Computer Interface system (BCI) has been put together to deal with this particular need. The fundamental presumption of the project reports the design, building as well as a testing imitation of a man's arm which is designed to be dynamically as well as kinematically accurate. The delivered device tries to resemble the motion of the biological human hand by analyzing the signals produced by brain waves. The brain waves are actually sensed by sensors in the Neurosky headset and generate alpha, beta, and gamma signal. Then this signal is analyzed by the microcontroller and is then inherited on to the synthetic hand via servo motors. A patient that suffers from an amputee below the elbow can gain from this particular bio robotic arm.

- by IJRASET Publication
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- Brain-computer interfaces, BCI, Robotic Arm, Brainwaves

The most usually applied therapy for stroke rehabilitation is based on functional task repetition. As an alternative therapy Brain-Computer Interface (BCI) based on motor imagery (MI) are used. This paper presents a BCI system designed and implemented for detecting brain activity related to left/right hand MI. Electroencephalography signal was recorded using EPOC device and it was processed with free software OpenViBE. A control signal that commands a visual application which gives feedback to the subject was generated. Two calibration methods were included: calibration with current information and calibration with prior information. A system's evaluation was conducted on two volunteers without neurological sequelae, for each one of them the accuracy was calculated and a MI index which reflects the subject's ability to imagine movement was estimated. The mean accuracy for daily calibration was 0.59 and for continue calibration was 0.68. The mean MI index for one subject was 0.82 and for the second subject was 0.94. These preliminaries results suggest that the developed system could be used to detect brain activity related to MI.

- by Ramiro Gatti
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- Rehabilitation, BCI, Stroke, Motor Imagery

Versione pre-print. Il testo a stampa è in: Mario Gerosa (ed.), CINEMA E TECNOLOGIA, Recco-Genova, Le Mani 2011, pp. 15-29.
La mutazione digitale del visuale che dopo il cinema investe i media e, più in generale, lo scenario tecno-scientifico come quello neurobiologico, spinge teorici e artisti verso paradigmi e linguaggi più adeguati, soprattutto nel cinema. Permane qualche nodo da sciogliere intorno alla continuità o discontinuità con l’analogico, considerato in modo confuso o stereotipato. Peraltro nella letteratura scientifica, come nelle narrative cyberpunk e steampunk di film e romanzi, si moltiplicano i rimandi ai fondamenti delle immagini nel precinema, se non nell’antichità classica. Come in un remake della trilogia "Ritorno al futuro"di Zemeckis. Se è indubbio, cioè, che la cibernetica di Turing e Wiener ha modificato il processamento dei dati, ribaltando i rapporti dell’uomo con la tecnica, il futuro presente di cinema e arti multimediali sembra un pendolo, un corto circuito tra preistoria e fantascienza. Alcune linee di progettazione, presenti nei laboratori di neuroscienze e neuroingegneria tra nuove tecnologie come le interfacce cervello- computer o BCI e come i robot e gli avatar per la comunicazione, sono rintracciabili, oltreché nel cinema di fantascienza, nella storia dell’arte e nell’archeologia dei media.

- by Elio Girlanda
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- BCI

Brain–machine interfaces (BMIs) enable humans to interact with devices by modulating their brain signals. Despite impressive technological advancements , several obstacles remain. The most commonly used BMI control signals are derived from the brain areas involved in primary sensory-or motor-related processing. However, these signals only reflect a limited range of human intentions. Therefore, additional sources of brain activity for controlling BMIs need to be explored. In particular, higher-order cognitive brain signals, specifically those encoding goal-directed intentions, are natural candidates for enlarging the repertoire of BMI control signals and making them more efficient and intuitive. Thus, here, we identify the prefrontal brain area as a key target region for future BMIs, given its involvement in higher-order, goal-oriented cognitive processes. Brain–Machine Interfaces: An Overview

- by Byoung-Kyong Min
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- EEG, Prefrontal Cortex, BCI, BMI

Millions of people worldwide suffer from diseases that lead to paralysis through disruption of signal pathways between the brain and the muscles. Neuroprosthetic devices are designed to restore lost function and could be used to form an electronic 'neural bypass' to circumvent disconnected pathways in the nervous system. It has previously been shown that intracortically recorded signals can be decoded to extract information related to motion, allowing non-human primates and paralysed humans to control computers and robotic arms through imagined movements 1–11. In non-human primates, these types of signal have also been used to drive activation of chemically paralysed arm muscles 12,13. Here we show that intracortically recorded signals can be linked in real-time to muscle activation to restore movement in a paralysed human. We used a chronically implanted intracortical microelectrode array to record multiunit activity from the motor cortex in a study participant with quadriplegia from cervical spinal cord injury. We applied machine-learning algorithms to decode the neuronal activity and control activation of the participant's forearm muscles through a custom-built high-resolution neuromuscular electrical stimulation system. The system provided isolated finger movements and the participant achieved continuous cortical control of six different wrist and hand motions. Furthermore, he was able to use the system to complete functional tasks relevant to daily living. Clinical assessment showed that, when using the system, his motor impairment improved from the fifth to the sixth cervical (C5–C6) to the seventh cervical to first thoracic (C7–T1) level unilaterally, conferring on him the critical abilities to grasp, manipulate, and release objects. This is the first demonstration to our knowledge of successful control of muscle activation using intracortically recorded signals in a paralysed human. These results have significant implications in advancing neuroprosthetic technology for people worldwide living with the effects of paralysis. The study participant was a 24-year-old male with stable, non-spastic C5/C6 quadriplegia from cervical spinal cord injury (SCI) sustained in a diving accident 4 years previously. He underwent implantation of a Utah microelectrode array (Blackrock Microsystems) in his left primary motor cortex. As shown in Fig. 1a, the hand area of the primary motor cortex was identified preopera-tively by performing functional magnetic resonance imaging (fMRI) while the participant attempted to mirror videos of hand movements. The final array implantation location was chosen during surgery, targeting the hand area while avoiding sulci and injury to large cor-tical vessels. The implant location was confirmed by co-registration of postoperative computed tomography imaging with preoperative fMRI (Fig. 1a) and is consistent with the 'knob' region of the primary motor cortex 5,14. The participant attended up to three sessions weekly for 15 months after implantation to use the neural bypass system (NBS). In each session, he was trained to utilize his motor cortical neuronal activity to control a custom-built high-resolution neuromuscular electrical stim-ulator (NMES). The NMES delivered electrical stimulation to his para-lysed right forearm muscles using an array of 130 electrodes embedded in a custom-made flexible sleeve wrapped around the arm (Fig. 1b). The participant was positioned in front of a computer monitor, and a stereo camera was placed overhead to track and record hand movements (Fig. 1c). During the study, up to 50 single units could be isolated in a given session. Near the end of the study, 33 units could be isolated with a mean signal-to-noise ratio of 3.05 ± 0.81 (mean ± s.d.) including units that responded to imagined or performed wrist movements (Fig. 1d). (See Extended Data Fig. 1 for additional unit activity.) Wavelet decomposition of the multiunit activity recorded from 96 microelec-trodes was used to produce mean wavelet power (MWP) features for decoding (Fig. 1e) (see Methods). To assess the ability of the NBS to restore individual movements, we focused on six wrist and hand movements that were all impaired by the participant's injury and reactivated by stimulation of forearm muscles (see Supplementary Video 1 showing the participant attempting the six movements without the use of the NBS). Each session began with recalibration of the NMES to map electrode stimulation patterns to evoked movements (see Methods). Cortical activity was continuously decoded as the participant attempted the six selected movements inter-leaved with rest periods, as cued by an animated virtual hand on the computer monitor. Changes in the MWP patterns for each movement were captured during the test. These patterns were then processed by multiple simultaneous neural decoders, one for each trained movement , using a nonlinear kernel method with a non-smooth support vector machine 15. The decoders were trained in successive blocks and, once trained, their outputs were continuously compared using the highest decoder output to control the corresponding NMES movement stimulation pattern (see Methods). During movement, a large portion of the stimulation artefact that occurred during a stimulation pulse was removed, but stimulation effects still remained (see Methods). To test the system's performance, test blocks were performed consisting of five trials of each of the six trained movements presented in random order. At the beginning of each trial, the participant was visually cued by the virtual hand demonstrating a target movement. Representative data, including modulation of MWP (before and after stimulation begins), decoder outputs, and corresponding movement state are shown in Fig. 2. MWP increases by a factor of 2–8 after stimulation begins because of residual stimulation artefact (see Methods and Extended Data Fig. 2). However, since the neural decoders were trained with MWP from before and during stimulation, they were able

- by Marcie Bockbrader and +2
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- Functional Electrical Stimulation, BCI
- by Christoph Guger
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- Brain Computer Interface, BCI
- by Ramakrishnan Angarai Ganesan
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- Transfer Learning, Non-Invasive BCI, BCI, EEG Signal Processing
- by Rabab Ward
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- Engineering, Algorithms, Brain Computer Interface, BCI

Noor: A Brain Opera is the first full opera where a performer, wearing a wireless EEG headset, triggers videos, a sonic environment, and a libretto with her brainwaves. The brainwaves are also displayed in live-time as the performer interacts with audience members within the confines of a 360-degree immersive theatre, and a dramatic story is narrated between the performer and me. Noor had its world premiere at ISEA 2016 Hong Kong (International Society for Electronic Art) on May 18, 2016. As I conceived and directed the work, its theme was loosely framed around the following metaphorical question: Is there a place in human consciousness where surveillance cannot go? Through images, sonic environment, pre-recorded libretto, and spoken narrative, the opera presented the true story of the life of Noor Inayat Khan, a Sufi Muslim Princess and covert British operative inside Nazi-occupied France, who was murdered at Dachau. It touched upon issues of memory, faith, and the locus of self in light of increasingly invasive and sophisticated surveillance technologies. The performance also worked with the bodies of both the performer and participants by creating a responsive feedback loop in which the performer's interactions with the audience—through movement, gaze, touch, and speech—visibly changed the performer's brainwaves. Noor: A Brain Opera explores the life of a young woman, Noor Inayat Khan, whose father Hazarat Inayat Khan brought Sufism to the West early in the twentieth century. Noor, born in Moscow of an American mother and Indian father, grew up in a peaceful household outside of Paris. When she was thirteen her father suddenly passed away while on a pilgrimage in India, leaving Noor and her mother in charge of her younger siblings. World War II broke out and the family moved to England for safety. Unable to stand idly by as the war unfolded, Noor as a young adult entered British secret intelligence training as a covert wireless

- by Christoph Bublitz and +1
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- Philosophy of Action, Neuroethics, Freedom of thought, Neurolaw

Background: Brain-Computer Interface (BCI) is becoming more reliable, thanks to the
advantages of Artificial Intelligence (AI). Recently, hybrid Deep Learning (hDL), which combines
different DL algorithms, has gained momentum over the past five years. In this work, we proposed a
review on hDL-based BCI starting from the seminal studies in 2015. Objectives: We have reviewed
47 papers that apply hDL to the BCI system published between 2015 and 2020 extracting trends and
highlighting relevant aspects to the topic. Methods: We have queried four scientific search engines
(Google Scholar, PubMed, IEEE Xplore and Elsevier Science Direct) and different data items were
extracted from each paper such as the database used, kind of application, online/offline training,
tasks used for the BCI, pre-processing methodology adopted, type of normalization used, which
kind of features were extracted, type of DL architecture used, number of layers implemented and
which optimization approach were used as well. All these items were then investigated one by one to
uncover trends. Results: Our investigation reveals that Electroencephalography (EEG) has been the
most used technique. Interestingly, despite the lower Signal-to-Noise Ratio (SNR) of the EEG data
that makes pre-processing of that data mandatory, we have found that the pre-processing has only
been used in 21.28% of the cases by showing that hDL seems to be able to overcome this intrinsic
drawback of the EEG data. Temporal-features seem to be the most effective with 93.94% accuracy,
while spatial-temporal features are the most used with 33.33% of the cases investigated. The most
used architecture has been Convolutional Neural Network-Recurrent Neural Network CNN-RNN
with 47% of the cases. Moreover, half of the studies have used a low number of layers to achieve a
good compromise between the complexity of the network and computational efficiency. Significance:
To give useful information to the scientific community, we make our summary table of hDL-based
BCI papers available and invite the community to published work to contribute to it directly. We have
indicated a list of open challenges, emphasizing the need to use neuroimaging techniques other than
EEG, such as functional Near-Infrared Spectroscopy (fNIRS), deeper investigate the advantages and
disadvantages of using pre-processing and the relationship with the accuracy obtained. To implement
new combinations of architectures, such as RNN-based and Deep Belief Network DBN-based, it is
necessary to better explore the frequency and temporal-frequency features of the data at hand.

- by Nibras Abo Alzahab and +2
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- Non-Invasive BCI, BCI, EEG Signal Processing, Deep Learning

In this chapter, several decoding methods for the Steady State Visual Evoked Potential (SSVEP) paradigm are discussed, as well as their use in Brain Computer Interfaces (BCIs). The chapter starts with the concept of BCI, the different categories and their relevance for speech- and motor disabled patients. The SSVEP paradigm is explained in detail. The discussed processing and decoding methods employ either time-domain or spectral domain features. Finally, to show the usability of these methods and of SSVEP-based BCIs in general, three applications are described: a spelling system, the ``Maze'' game and the ``Tower Defense'' game. We conclude the chapter by addressing some challenges for future research.

- by Nikolay Chumerin
- •
- Brain-computer interfaces, Video Games, Digital Signal Processing, EEG
- by Marc Van Hulle
- •
- Video Games, Non-Invasive BCI, BCI, Brain Computer Interfaces

L’indagine, sia di natura progettuale che di riflessione critica sulla tematica delle BCI, ha affrontato in termini procedurali e strumentali le diverse fasi del progetto parametrico, provando a definire un nuovo modello produttivo postdigitale (Alexenberg, 2011), altamente tecnologico e al contempo legato all’uomo. Il senso della ricerca, tuttora in corso, sta nel tentativo di costruire nuovi modelli postdigitali, che si muovono al confine tra le discipline e che partono dall’uomo per ritornare sull’uomo, sottoforma di dispositivi di un pensiero
progettuale più ampio e complesso. Gli artefatti realizzati
non adempiono il loro ruolo nel rispondere a una precisa
funzione pratica, ma piuttosto aprono una direzione progettuale
per un diverso approccio alla manifattura 3D.

— The efficient control of our body and successful interaction with the environment are possible through the integration of multisensory information. Brain-computer interface (BCI) may allow people with sensorimotor disorders to actively interact in the world. In this study, visual information was paired with auditory feedback to improve the BCI control of a humanoid surrogate. Healthy and spinal cord injured (SCI) people were asked to embody a humanoid robot and complete a pick-and-place task by means of a visual evoked potentials BCI system. Participants observed the remote environment from the robot's perspective through a head mounted display. Human-footsteps and computer-beep sounds were used as synchronous/asynchronous auditory feedback. Healthy participants achieved better placing accuracy when listening to human footstep sounds relative to a computer-generated sound. SCI people demonstrated more difficulty in steering the robot during asynchronous auditory feedback conditions. Importantly, subjective reports highlighted that the BCI mask overlaying the display did not limit the observation of the scenario and the feeling of being in control of the robot. Overall, the data seem to suggest that sensorimotor-related information may improve the control of external devices. Further studies are required to understand how the contribution of residual sensory channels could improve the reliability of BCI systems.

- by Gabriele Fusco
- •
- Spinal Cord Injury, BCI

Recent research has reached a consensus on the feasibility of motor imagery brain-computer interface (MI-BCI) for different applications, especially in stroke rehabilitation. Most MI-BCI systems rely on temporal, spectral, and spatial features of single channels to distinguish different MI patterns. However, no successful communication has been established for a completely locked-in subject. To provide more useful and informative features, it has been recommended to take into account the relationships among electroencephalographic (EEG) sensor/source signals in the form of brain connectivity as an efficient tool of neuroscience. In this review, we briefly report the challenges and limitations of conventional MI-BCIs. Brain connectivity analysis, particularly functional and effective, has been described as one of the most promising approaches for improving MI-BCI performance. An extensive literature on EEG-based MI brain connectivity analysis of healthy subjects is reviewed. We subsequently discuss the brain connectomes during left and right hand, feet, and tongue MI movements. Moreover, key components involved in brain connectivity analysis that considerably affect the results are explained. Finally, possible technical shortcomings that may have influenced the results in previous research are addressed and suggestions are provided.

- by Mahyar Hamedi and +2
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- Neuroscience, Brain-computer interfaces, EEG, Non-Invasive BCI
- by Scott Makeig
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- Biomedical Engineering, Machine Learning, Signal Processing, Neuroimaging
- by Fouad Chouag
- •
- Robotics, Neuroscience, Computer Science, Artificial Intelligence

An approach is presented in this paper for automated estimation of human emotions from combination of multimodal data: electroencephalogram and facial images. The used EEG features are the Hjorth parameters calculated for theta, alpha, beta and gamma bands taken from pre-defined channels. For face emotion estimation PCA feature are selected. Classification is performed with support vector machines. Since the human emotions are modelled as combinations from physiological elements such as arousal, valence, dominance, liking, etc., these quantities are the classifier's outputs. The best achieved correct classification performance for EEG is about 76%. Classifier combination is used to return the final score for the particular subject.

- by Elena Sajno
- •
- Neuroscience, Psychology, Neuropsychology, Brain-computer interfaces
- by Gabriele Fusco and +1
- •
- Spinal Cord Injury, BCI, Tendon Vibration
- by Vincenzo Silani
- •
- Assessment, Eye tracking, Psychometrics, Measurement and Evaluation

Background. Previous work has demonstrated that a commercial gaming
electroencephalography (EEG) system, Emotiv EPOC, can be adjusted to provide
valid auditory event-related potentials (ERPs) in adults that are comparable to ERPs
recorded by a research-grade EEG system, Neuroscan. The aim of the current study
was to determine if the same was true for children.
Method. An adapted Emotiv EPOC system and Neuroscan system were used to
make simultaneous EEG recordings in nineteen 6- to 12-year-old children under
“passive” and “active” listening conditions. In the passive condition, children were
instructed to watch a silent DVD and ignore 566 standard (1,000 Hz) and 100 deviant
(1,200 Hz) tones. In the active condition, they listened to the same stimuli, and were
asked to count the number of ‘high’ (i.e., deviant) tones.
Results. Intraclass correlations (ICCs) indicated that the ERP morphology recorded
with the two systems was very similar for the P1, N1, P2, N2, and P3 ERP peaks
(r = .82 to .95) in both passive and active conditions, and less so, though still strong,
for mismatch negativity ERP component (MMN; r = .67 to .74). There were few
differences between peak amplitude and latency estimates for the two systems.
Conclusions. An adapted EPOC EEG system can be used to index children’s late
auditory ERP peaks (i.e., P1, N1, P2, N2, P3) and theirMMNERP component.

- by Nicholas A Badcock and +5
- •
- EEG, Non-Invasive BCI, ERP, BCI

Motor imagery techniques are largely used in asynchronous BCI for the control of external devices. In this work we comparatively evaluate the performance of different state-of arts BCI algorithms, based on the Common Spatial Pattern approach, under different sensory feedback conditions. In particular the role of tendon vibration, inducing illusory movement, is analyzed in the context of BCI for motor imagery.

- by Michele Barsotti
- •
- Brain-computer interfaces, BCI, Proprioception, EEG Signal Processing
- by Eloisa Vargiu
- •
- Quality of life, Context-Aware Computing, BCI, Telemonitoring in medicine
- by Mariam Al-Sagban
- •
- Non-Invasive BCI, P300, BCI