This reprint is dedicated to the study of brain activity related to emotional and attentional involvement as measured by Brain–computer interface (BCI) systems designed for different purposes. A BCI system can translate brain signals (e.g., electric or hemodynamic brain activity indicators) into a command to execute an action in the BCI application (e.g., a wheelchair, the cursor on the screen, a spelling device or a game). These tools have the advantage of having real-time access to the ongoing brain activity of the individual, which can provide insight into the user’s emotional and attentional states by training a classification algorithm to recognize mental states. The success of BCI systems in contemporary neuroscientific research relies on the fact that they allow one to “think outside the lab”. The integration of technological solutions, artificial intelligence and cognitive science allowed and will allow researchers to envision more and more applications for the future. The clinical and everyday uses are described with the aim to invite readers to open their minds to imagine potential further developments.

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The field of Brain–Computer Interfaces (BCIs) has grown rapidly in the last few decades, allowing the development of faster and more reliable assistive technologies based on direct links between the brain and an external device. Novel applications of BCIs have also been proposed, especially in the area of human augmentation, i.e., enabling people to go beyond human limitations in sensory, cognitive and motor tasks. Brain-imaging techniques, such as electroencephalography, have been used to extract neural correlates of various brain processes and transform them, via machine learning, into commands for external devices. Brain stimulation technology has allowed to trigger the activation of specific brain areas to enhance the cognitive processes associated to the task at hand, hence improving performance. BCIs have therefore extended their scope from assistive technologies for people with disabilities to neuro-tools for human enhancement. This Special Issue aims at showing the recent advances in BCIs for human augmentation, highlighting new results on both traditional and novel applications. These include, but are not limited to, control of external devices, communication, cognitive enhancement, decision making and entertainment.

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This book includes 23 published papers on Special issues of "Image and Video Processing and Recognition Based on Artificial Intelligence" in the journal Sensors. The purpose of this Special Issue was to invite high-quality and state-of-the-art academic papers on challenging issues in the field of AI-based image and video processing and recognition.

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The international conference on Intelligent Human Computer Interaction (IHCI) is a forum for the presentation of research results and technological advances at the crossroads of human-computer interaction, artificial intelligence, signal processing, and computer vision. It brings together engineers and scientists from around the world focussing on theoretical, practical, and applicational aspects of the field. The 9th event, IHCI 2017, took place during December 11th–13th, 2017 in Evry, France. The present proceedings consist of papers presented at the conference. The call for papers attracted 25 submissions from around the world, which have been reviewed by at least two and up to four members of the International Program Committee. Fifteen oral communications have been selected, the authors of which come from ten countries and four continents. The summary of one of the invited talks is also included. We thank all the invited speakers, authors, and members of the Program Committee for their contribution in making IHCI 2017 a stimulating and productive conference.

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The international conference on Intelligent Human Computer Interaction (IHCI) is a forum for the presentation of research results and technological advances at the crossroads of human-computer interaction, artificial intelligence, signal processing, and computer vision. It brings together engineers and scientists from around the world focussing on theoretical, practical, and applicational aspects of the field. The 9th event, IHCI 2017, took place during December 11th–13th, 2017 in Evry, France. The present proceedings consist of papers presented at the conference. The call for papers attracted 25 submissions from around the world, which have been reviewed by at least two and up to four members of the International Program Committee. Fifteen oral communications have been selected, the authors of which come from ten countries and four continents. The summary of one of the invited talks is also included. We thank all the invited speakers, authors, and members of the Program Committee for their contribution in making IHCI 2017 a stimulating and productive conference.

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brain computer interface; artificial intelligence; computer networks; classification databases; HCI machine learning; signal processing; user interfaces

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The primary purpose of Brain Computer Interface (BCI) systems is to help patients communicate with their environment or to aid in their recovery. A common denominator for all BCI patient groups is that they suffer from a neurological deficit. As a consequence, BCI systems in clinical and research settings operate with control signals (brain waves) that could be substantially altered compared to brain waves of able-bodied individuals. Most BCI systems are built and tested on able-bodied individuals, being insufficiently robust for clinical applications. The main reason for this is a lack of systematic analysis on how different neurological problems affect the BCI performance. Neurological problems interfering with BCI performance are either a direct cause of a disability (e.g. stroke, autism, epilepsy ) or secondary consequences of a disability, often overlooked in design of BCI systems (chronic pain, spasticity and antispastic drugs, loss of cognitive functions, drowsiness, medications which are increasing/decreasing brain activity in certain frequency range) . While some of these deficits may decrease the performance of a BCI, others may potentially improve its performance compared to BCI tested on a healthy population (e.g. overactivation of motor cortex in patients with Central neuropathic pain (CNP), increased alpha activity in some patient groups). Depending on the neurological condition, a prolonged modulation of brain waves through BCI might produce both positive or detrimental effects. Thus some BCI protocols might be more suitable for a short term use (e.g. rehabilitation of movement) while the others would be more suitable for a long term use. Prolonged self-regulation of brain oscillation through BCI could potentially be used as a treatment for aberrant brain connections for conditions ranging from motor deficits to Autism Spectrum Disorders (ASD). Currently, ASD is an increasingly prevalent condition in the U.S. with core deficits in imitation learning, language, empathy, theory of mind, and self-awareness. Understanding its neuroetiology is not only critical and necessary but should provide relevant insights into the relationship between neuroanatomy, physiology and behaviour. In this Research Topic we welcome studies of the highest scientific quality highlighting how BCI systems based on different principles (SSVEP, P300, slow cortical potential, auditory potential, operant conditioning, etc) interact with the underlying neurological problems and how performance of these BCI system differ compared to similar systems tested on healthy individuals. We also welcome studies defining signatures of neurological disorders and proposing BCI based treatments. We expect to generate a body of knowledge valuable both to researchers working with clinical populations, but also to a vast majority of BCI researchers testing new algorithms on able-bodied people. This should lead towards more robust or tailor-made BCI protocols, facilitating translation of research from laboratories to the end users.

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