A systematic review of hybrid brain-computer interfaces: Taxonomy and usability perspectives

Abstract

A new Brain-Computer Interface (BCI) technique, which is called a hybrid BCI, has recently been proposed to address the limitations of conventional single BCI system. Although some hybrid BCI studies have shown promising results, the field of hybrid BCI is still in its infancy and there is much to be done. Especially, since the hybrid BCI systems are so complicated and complex, it is difficult to understand the constituent and role of a hybrid BCI system at a glance. Also, the complicated and complex systems make it difficult to evaluate the usability of the systems. We systematically reviewed and analyzed the current state-of-the-art hybrid BCI studies, and proposed a systematic taxonomy for classifying the types of hybrid BCIs with multiple taxonomic criteria. After reviewing 74 journal articles, hybrid BCIs could be categorized with respect to 1) the source of brain signals, 2) the characteristics of the brain signal, and 3) the characteristics of operation in each system. In addition, we exhaustively reviewed recent literature on usability of BCIs. To identify the key evaluation dimensions of usability, we focused on task and measurement characteristics of BCI usability. We classified and summarized 31 BCI usability journal articles according to task characteristics (type and description of task) and measurement characteristics (subjective and objective measures). Afterwards, we proposed usability dimensions for BCI and hybrid BCI systems according to three core-constructs: Satisfaction, effectiveness, and efficiency with recommendations for further research. This paper can help BCI researchers, even those who are new to the field, can easily understand the complex structure of the hybrid systems at a glance. Recommendations for future research can also be helpful in establishing research directions and gaining insight in how to solve ergonomics and HCI design issues surrounding BCI and hybrid BCI systems by usability evaluation.

Modern Technologies in Studying the Mechanisms, Diagnostics, and Treatment of Autism Spectrum Disorders (Review)

“…One of them consists in the development of approaches that involve the use of musical or music-like feedback signals from one's own bioelectric characteristics, which facilitate the patient's perception of them and contribute to increasing the effectiveness of therapeutic procedures [92][93][94]. The second trend is related to the intention to develop multimodal [95] or hybrid [96] neurointerfaces, which use complex multimodal feedback not only from the parameters of the EEG but also from other body systems [97].…”

Hybrid Brain-Computer Interface Systems: Approaches, Features, and Trends

“…A systematic review of hybrid BCI was done by [57] in terms of taxonomy and usability. This review discussed two modes of operation: simultaneous and sequential modes.…”

“…As explained previously in [52], the hybrid BCI used simultaneous mode which includes ERD (imagined movement) to control the cursor in vertical position and SSVEP to control the cursor in horizontal position. In sequential mode, output of one BCI system is used as the input for another to control various functions of the second BCI system or as a switch in asynchronous mode [57]. These two modes are depicted in Figure 4a and b.…”

Towards optimal visual presentation design for hybrid EEG—fTCD brain–computer interfaces

“…In general, with the aim of improving the accuracy as well as the information transfer rate obtained using such BCI systems, recent studies suggested employing the concept of hybrid BCIs in which two or more systems are combined to infer user intent [13]. Hybrid BCIs can be categorized into 2 main classes including BCIs exploiting multimodal signals and BCIs exploiting different patterns to induce changes in the same brain physiological signal [14]. For the design of BCIs that belong to the first class, signals such as EEG, fTCD, fNIRS, fMRI, etc., are recorded simultaneously [15].…”

“…for the same physiological signal (EEG) Aya Khalaf*, Ervin Sejdic, Murat Akcakaya are used to build the hybrid BCI [17]. Both hybrid BCI categories can exploit multisensory stimulation (visual, auditory and tactile) depending on the employed recording modalities [14].…”