EEG hybrid brain-computer interfaces: A scoping review applying an existing hybrid-BCI taxonomy and considerations for pediatric applications

Abstract

Most hybrid brain-computer interfaces (hBCI) aim at improving the performance of single-input BCI. Many combinations are possible to configure an hBCI, such as using multiple brain input signals, different stimuli or more than one input system. Multiple studies have been done since 2010 where such interfaces have been tested and analyzed. Results and conclusions are promising but little has been discussed as to what is the best approach for the pediatric population, should they use hBCI as an assistive technology. Children might face greater challenges when using BCI and might benefit from less complex interfaces. Hence, in this scoping review we included 42 papers that developed hBCI systems for the purpose of control of assistive devices or communication software, and we analyzed them through the lenses of potential use in clinical settings and for children. We extracted taxonomic categories proposed in previous studies to describe the types of interfaces that have been developed. We also proposed interface characteristics that could be observed in different hBCI, such as type of target, number of targets and number of steps before selection. Then, we discussed how each of the extracted characteristics could influence the overall complexity of the system and what might be the best options for applications for children. Effectiveness and efficiency were also collected and included in the analysis. We concluded that the least complex hBCI interfaces might involve having a brain inputs and an external input, with a sequential role of operation, and visual stimuli. Those interfaces might also use a minimal number of targets of the strobic type, with one or two steps before the final selection. We hope this review can be used as a guideline for future hBCI developments and as an incentive to the design of interfaces that can also serve children who have motor impairments.

Pilot Experiments and Hardware Design of Smart Electrooculographic Headband for People With Muscular Paralysis

“…During the experiments, we thought about whether it would be possible to add an electroencephalographic (EEG) recording and create hybrid brain-computer interface (hBCI) with increased reliability of non-manual HMI [19], [20]. The TI ADS 1299 chip is compatible with EEG.…”

“…Additionally, the PPG sensor monitors heart rate (HR) and an advanced analysis of heart rate variability (HRV) is possible, which can give evidence of both the health and mental state of patients [17], [18]. Furthermore, thanks to the addition of EEG recording, it is possible to create hybrid brain-computer interface (hBCI) with increased reliability of non-manual human-machine interface (HMI) [19], [20].…”

Brain-computer interface paradigms and neural coding

“…To better or more fully characterize the relationship between the stimulus / mental activity and the neural response, a combination of several neural coding methods can be considered, which is a hybrid neural coding approach. To more accurately decode the stimulus or mental activity from the neural response, intracortical BCI hybrid coding schemes can combine two or more of the above models ( Choi et al, 2017 ; Mussi and Adams, 2022 ). For example, global features such as pitch or formant transition profiles can be represented by both rate coding and place coding ( Miller and Sachs, 1983 ).…”

“…For example, Abiri et al (2019) reviewed EEG-based BCI paradigms, and Xu et al (2021) reviewed the EEG-based BCI brain coding and decoding mechanisms. In addition to EEG-based BCI paradigms and neural coding, there are also other BCI paradigms and neural coding based on brain imaging techniques, such as intracortical local field potentials (LFP) ( Hochberg et al, 2012 ; Willett et al, 2021 , 2023 ), electroencephalogram (ECoG) ( Luo et al, 2022 ; Branco et al, 2023 ; Metzger et al, 2023 ), functional near-infrared spectroscopy (fNIRS) ( Abdalmalak et al, 2021 ; Paulmurugan et al, 2021 ; Eastmond et al, 2022 ), functional magnetic resonance imaging (fMRI) ( Naselaris et al, 2011 ; Du et al, 2019 ), magnetoencephalography (MEG) ( Xu et al, 2022 ; Bu et al, 2023 ), and hybrid brain-computer interface (hBCI) ( Choi et al, 2017 ; Mussi and Adams, 2022 ). Therefore, we systematically elaborated on the definition and design principles of the BCI paradigm as well as the definition and modeling principles of BCI neural coding and introduced the existing main BCI paradigms and neural coding.…”

Characterization of EEG-P300 Signals in Drug Users

“…EEG has several types of signals: Slow Cortical Potential (SCP), Event-Related Desynchronization (ERD), Steady-State Visual Evoked Potentials (SSVEP), and Event-related Potential [7,8]. ERP signals reflect the presence of bioelectric activity associated with stimulation processing [9].…”