The Space Between Us: Evaluating a multi-user affective brain-computer music interface

SSVEP-based brain-computer interface for music using a low-density EEG system

In this paper, we present a bespoke brain-computer interface (BCI), which was developed for a person with severe motor-impairments, who was previously a Violinist, to allow performing and composing music at home. It uses steady-state visually evoked potential (SSVEP) and adopts a dry, low-density, and wireless electroencephalogram (EEG) headset. In this study, we investigated two parameters: (1) placement of the EEG headset and (2) inter-stimulus distance and found that the former significantly improved the information transfer rate (ITR). To analyze EEG, we adopted canonical correlation analysis (CCA) without weight-calibration. The BCI for musical performance realized a high ITR of 37.59 ± 9.86 bits min-1 and a mean accuracy of 88.89 ± 10.09%. The BCI for musical composition obtained an ITR of 14.91 ± 2.87 bits min-1 and a mean accuracy of 95.83 ± 6.97%. The BCI was successfully deployed to the person with severe motor-impairments. She regularly uses it for musical composition at home, demonstrating how BCIs can be translated from laboratories to real-world scenarios.

Research on the Emotions Based on Brain-Computer Technology: A Bibliometric Analysis and Research Agenda

This study conducts a scientific analysis of 249 literature on the application of brain-computer technology in emotion research. We find that existing researches mainly focus on engineering, computer science, neurosciences neurology and psychology. PR China, United States, and Germany have the largest number of publications. Authors can be divided into four groups: real-time functional magnetic resonance imaging (rtfMRI) research group, brain-computer interface (BCI) impact factors analysis group, brain-computer music interfacing (BCMI) group, and user status research group. Clustering results can be divided into five categories, including external stimulus and event-related potential (ERP), electroencephalography (EEG), and information collection, support vector machine (SVM) and information processing, deep learning and emotion recognition, neurofeedback, and self-regulation. Based on prior researches, this study points out that individual differences, privacy risk, the extended study of BCI application scenarios and others deserve further research.

Wired Emotions: Ethical Issues of Affective Brain-Computer Interfaces

Ethical issues concerning brain-computer interfaces (BCIs) have already received a considerable amount of attention. However, one particular form of BCI has not received the attention that it deserves: Affective BCIs that allow for the detection and stimulation of affective states. This paper brings the ethical issues of affective BCIs in sharper focus. The paper briefly reviews recent applications of affective BCIs and considers ethical issues that arise from these applications. Ethical issues that affective BCIs share with other neurotechnologies are presented and ethical concerns that are specific to affective BCIs are identified and discussed.

Accessible Digital Musical Instruments—A Review of Musical Interfaces in Inclusive Music Practice

Current advancements in music technology enable the creation of customized Digital Musical Instruments (DMIs). This paper presents a systematic review of Accessible Digital Musical Instruments (ADMIs) in inclusive music practice. History of research concerned with facilitating inclusion in music-making is outlined, and current state of developments and trends in the field are discussed. Although the use of music technology in music therapy contexts has attracted more attention in recent years, the topic has been relatively unexplored in Computer Music literature. This review investigates a total of 113 publications focusing on ADMIs. Based on the 83 instruments in this dataset, ten control interface types were identified: tangible controllers, touchless controllers, Brain–Computer Music Interfaces (BCMIs), adapted instruments, wearable controllers or prosthetic devices, mouth-operated controllers, audio controllers, gaze controllers, touchscreen controllers and mouse-controlled interfaces. The majority of the AMDIs were tangible or physical controllers. Although the haptic modality could potentially play an important role in musical interaction for many user groups, relatively few of the ADMIs (14.5%) incorporated vibrotactile feedback. Aspects judged to be important for successful ADMI design were instrument adaptability and customization, user participation, iterative prototyping, and interdisciplinary development teams.

"Enheduanna-A Manifesto of Falling" Live Brain-Computer Cinema Performance: Performer and Audience Participation, Cognition and Emotional Engagement Using Multi-Brain BCI Interaction

The fields of neural prosthetic technologies and Brain-Computer Interfaces (BCIs) have witnessed in the past 15 years an unprecedented development, bringing together theories and methods from different scientific fields, digital media, and the arts. More in particular, artists have been amongst the pioneers of the design of relevant applications since their emergence in the 1960s, pushing the boundaries of applications in real-life contexts. With the new research, advancements, and since 2007, the new low-cost commercial-grade wireless devices, there is a new increasing number of computer games, interactive installations, and performances that involve the use of these interfaces, combining scientific, and creative methodologies. The vast majority of these works use the brain-activity of a single participant. However, earlier, as well as recent examples, involve the simultaneous interaction of more than one participants or performers with the use of Electroencephalography (EEG)-based multi-brain BCIs. In this frame, we discuss and evaluate “Enheduanna—A Manifesto of Falling,” a live brain-computer cinema performance that enables for the first time the simultaneous real-time multi-brain interaction of more than two participants, including a performer and members of the audience, using a passive EEG-based BCI system in the context of a mixed-media performance. The performance was realised as a neuroscientific study conducted in a real-life setting. The raw EEG data of seven participants, one performer and two different members of the audience for each performance, were simultaneously recorded during three live events. The results reveal that the majority of the participants were able to successfully identify whether their brain-activity was interacting with the live video projections or not. A correlation has been found between their answers to the questionnaires, the elements of the performance that they identified as most special, and the audience's indicators of attention and emotional engagement. Also, the results obtained from the performer's data analysis are consistent with the recall of working memory representations and the increase of cognitive load. Thus, these results prove the efficiency of the interaction design, as well as the importance of the directing strategy, dramaturgy and narrative structure on the audience's perception, cognitive state, and engagement.