In this review, we consider the potential functional role of beta-band oscillations, which at present is not yet well understood. We discuss evidence from recent studies on top-down mechanisms involved in cognitive processing, on the motor system and on the pathophysiology of movement disorders that suggest a unifying hypothesis: beta-band activity seems related to the maintenance of the current sensorimotor or cognitive state. We hypothesize that beta oscillations and/or coupling in the beta-band are expressed more strongly if the maintenance of the status quo is intended or predicted, than if a change is expected. Moreover, we suggest that pathological enhancement of beta-band activity is likely to result in an abnormal persistence of the status quo and a deterioration of flexible behavioural and cognitive control.

Beta-band oscillations--signalling the status quo?

Advances in brain science and computer technology in the past decade have led to exciting developments in brain-computer interface (BCI), thereby making BCI a top research area in applied science. The renaissance of BCI opens new methods of neurorehabilitation for physically disabled people (e.g. paralyzed patients and amputees) and patients with brain injuries (e.g. stroke patients). Recent technological advances such as wireless recording, machine learning analysis, and real-time temporal resolution have increased interest in electroencephalographic (EEG) based BCI approaches. Many BCI studies have focused on decoding EEG signals associated with whole-body kinematics/kinetics, motor imagery, and various senses. Thus, there is a need to understand the various experimental paradigms used in EEG-based BCI systems. Moreover, given that there are many available options, it is essential to choose the most appropriate BCI application to properly manipulate a neuroprosthetic or neurorehabilitation device. The current review evaluates EEG-based BCI paradigms regarding their advantages and disadvantages from a variety of perspectives. For each paradigm, various EEG decoding algorithms and classification methods are evaluated. The applications of these paradigms with targeted patients are summarized. Finally, potential problems with EEG-based BCI systems are discussed, and possible solutions are proposed.

A comprehensive review of EEG-based brain-computer interface paradigms.

Brain computer interfacing: Applications and challenges

For generations, humans have fantasized about the ability to create devices that can see into a persons mind and thoughts, or to communicate and interact with machines through thought alone. Such ideas have long captured the imagination of humankind in the form of ancient myths and modern science fiction stories. Recent advances in cognitive neuroscience and brain imaging technologies have started to turn these myths into a reality, and are providing us with the ability to interface directly with the human brain. This ability is made possible through the use of sensors that monitor physical processes within the brain which correspond with certain forms of thought. Brain-Computer Interfaces: Applying our Minds to Human-Computer Interaction broadly surveys research in the Brain-Computer Interface domain. More specifically, each chapter articulates some of the challenges and opportunities for using brain sensing in Human-Computer Interaction work, as well as applying Human-Computer Interaction solutions to brain sensing work. For researchers with little or no expertise in neuroscience or brain sensing, the book provides background information to equip them to not only appreciate the state-of-the-art, but also ideally to engage in novel research. For expert Brain-Computer Interface researchers, the book introduces ideas that can help in the quest to interpret intentional brain control and develop the ultimate input device. It challenges researchers to further explore passive brain sensing to evaluate interfaces and feed into adaptive computing systems. Most importantly, the book will connect multiple communities allowing research to leverage their work and expertise and blaze into the future.

/pdf/brain-computer-interfaces-applying-our-minds-to-human-45p87wj49n.pdf

Brain-Computer Interfaces: Applying our Minds to Human-Computer Interaction

Over the past several decades, electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have attracted attention from researchers in the field of neuroscience, neural engineering, and clinical rehabilitation. While the performance of BCI systems has improved, they do not yet support widespread usage. Recently, visual and auditory BCI systems have become popular because of their high communication speeds, little user training, and low user variation. However, building robust and practical BCI systems from physiological and technical knowledge of neural modulation of visual and auditory brain responses remains a challenging problem. In this paper, we review the current state and future challenges of visual and auditory BCI systems. First, we describe a new taxonomy based on the multiple access methods used in telecommunication systems. Then, we discuss the challenges of translating current technology into real-life practices and outline potential avenues to address them. Specifically, this review aims to provide useful guidelines for exploring new paradigms and methodologies to improve the current visual and auditory BCI technology.

https://sccn.ucsd.edu/~yijun/pdfs/TBME14.pdf

Visual and Auditory Brain–Computer Interfaces

Research in brain-computer interface (BCI) has significantly increased during the last few years. Additionally to their initial role as assisting devices for the physically challenged, BCIs are now proposed for a wider range of applications. As any human-machine interaction system, BCIs can benefit from adapting their operation to the emotional state of the user. BCIs already have access to the brain activity, which provides significant insight into the user's emotional state. This information can be utilised in two manners. (1) Knowledge of the influence of the emotional state on brain activity patterns can allow the BCI to adapt its recognition algorithms, so that the intention of the user is correctly interpreted in spite of signal deviations induced by the subject's emotional state. (2) The ability to recognise emotions can be used to provide the user with more natural ways of controlling the BCI through affective modulation and can potentially lead to higher communication throughput.

Emotional brain-computer interfaces

Research in Brain-Computer Interface (BCI) has significantly increased during the last few years. In addition to their initial role as assisting devices for the physically challenged, BCIs are now proposed for a wider range of applications. As in any HCI application, BCIs can also benefit from adapting their operation to the emotional state of the user. BCIs have the advantage of having access to brain activity which can provide significant insight into the user's emotional state. This information can be utilized in two manners. 1) Knowledge of the influence of the emotional state on brain activity patterns can allow the BCI to adapt its recognition algorithms, so that the intention of the user is still correctly interpreted in spite of signal deviations induced by the subject's emotional state. 2) The ability to recognize emotions can be used in BCIs to provide the user with more natural ways of controlling the BCI through affective modulation. Thus, controlling a BCI by recollecting a pleasant memory can be possible and can potentially lead to higher information transfer rates. These two approaches of emotion utilization in BCI are elaborated in detail in this paper in the framework of non-invasive EEG based BCIs.

Objective Recent evidence reports cognitive, metabolic, and sleep restoration benefits resulting from the enhancement of sleep slow-waves using auditory stimulation Our objective is to make this concept practical for consumer use by developing and validating an electroencephalogram (EEG) closed-loop system to deliver auditory stimulation during sleep to enhance slow-waves Approach The system automatically detects slow-wave sleep with 74% sensitivity and 97% specificity and optimally delivers stimulation in the form of 50 ms-long tones separated by a constant one-second inter-tone interval at a volume that is dynamically modulated such that louder tones are delivered when sleep is deeper The system was tested in a study involving 28 participants (18F, 10M; 369  ±  73 years old; median age: 40 years old) who used the system for ten nights (five nights in a sham condition and five in a stimulation condition) Four nights in each condition were recorded at-home and the fifth one in-lab Main results The analysis in two age groups defined by the median age of participants in the study shows significant slow wave activity enhancement (+161%, p  <  001) for the younger group and absence of effect on the older group However, the older group received only a fraction (57%) of the stimulation compared to the younger group Changes in sleep architecture and EEG properties due to aging have influenced the amount of stimulation The analysis of the stimulation timing suggests an entrainment-like phenomenon where slow-waves align to the stimulation periodicity In addition, enhancement of spindle power in the stimulation condition was found Significance We show evidence of the viability of delivering auditory stimulation during sleep, at home, to enhance slow wave activity The system ensures the stimulation delivery to be at the right time during sleep without causing disturbance

https://iopscience.iop.org/article/10.1088/1741-2552/aae18f/pdf

Closed-loop system to enhance slow-wave activity

The movie industry produces thousands of feature films and TV series annually. Such massive data volumes would take consumers more than a lifetime to watch. Therefore, summarization of narrative media, which engages in providing concise and informative video summaries, has become a popular topic of research. However, most of the summarization solutions so far aim to represent just the overall atmosphere of the video at the expense of the story line. In this paper we describe a novel approach for automated creation of summaries for narrative videos. We propose an automated content analysis and summarization framework for creating moving-image summaries. We aim at preserving the story line to the level that users can watch the summary instead of the original content. Our solution is based on textual cues available in subtitles and movie scripts. We extract features like keywords, main characters names and presence, and combine them in an importance function to identify the moments most relevant for preserving the story line. We develop several summarization methods and evaluate the quality of the resulting summaries in terms of user understanding and user satisfaction through a user test.

Automated summarization of narrative video on a semantic level

In this work we evaluate the possibility of predicting the emotional state of a person based on the EEG. We investigate the problem of classifying valence from EEG signals during the presentation of affective pictures, utilizing the ”frontal EEG asymmetry” phenomenon. To distinguish positive and negative emotions, we applied the Common Spatial Patterns algorithm. In contrast to our expectations, the affective pictures did not reliably elicit changes in frontal asymmetry. The classifying task thereby becomes very hard as reflected by the poor classifier performance. We suspect that the masking of the source of the brain activity related to emotions, coming mostly from deeper structures in the brain, and the insufficient emotional engagement are among main reasons why it is difficult to predict the emotional state of a person.

Tsvetomira Tsoneva

Papers

Emotional brain-computer interfaces

Emotional brain-computer interfaces

Closed-loop system to enhance slow-wave activity

Automated summarization of narrative video on a semantic level

Frontal EEG Asymmetry Based Classification of Emotional Valence using Common Spatial Patterns