2.2. Enhanced Hybrid Brain-Computer Interfaces

Hybrid Brain-Computer Interfaces (hBCIs) that integrate EEG and ECG signals are emerging as powerful tools to help enhance user control and adaptability in various applications, such as assistive technologies, prosthetic devices, communication systems and health monitoring. By combining heart and brain signals, these complementary physiological responses help create more nuanced interfaces that improve accuracy, reduce user burden and support adaptive responses.

Shahid et al. [] investigated how combining EEG and ECG signals help improve the accuracy of motor imagery (MI)-based tasks in BCI applications. Their research showed that adding ECG data to EEG improved the detection of MI tasks through incorporating cardiac response to mental effort, which was reflected by an increased in heart rate during MI tasks. The finding suggested that ECG can provide additional context for BCI systems, especially for cases where accurate detection of user’s intention is critical, for example, for assistive devices for individuals with limited mobility [].

Study by Sato et al. [] explored a simplified single-lead hybrid BCI setup, where both EEG and ECG signals were recorded through one channel. This aimed to reduce the setup complexity and improve user comfort. By using this simple single-lead system, they were able to achieve high accuracy in character prediction for a P300-based speller application. This suggested that single-lead hBCIs can offer a less intrusive yet effective approach for communication.

On the other hand, Scherer et al. [] studied the use of heart rate responses as a “brain switch” to enable self-initiated control in BCI systems. By ultilizing the heart rate changes that were induced by breathing patterns, users were able to autonomously activate and deactivate the BCI, offering a hands-free control mechanism. This functionality is especially helpful in environments where physical interaction is limited, suggesting potential for user autonomy enhancement.

Pfurtscheller et al. [] further expanded on the concept of hBCIs by proposing systems that integrate multiple physiological signs, including EEG and ECG signals, to improve accuracy. Their work demonstrated that combining event-related dysynchronization (ERD) and steady-state visual evoked potential (SSVEP) signals, with additional physiological inputs like heart rate, could improve system realibility. For example, in a hybrid BCI for controlling a hand orthosis, the combined approach led to more accurate control with fewer unintended activations, which is important in assistive technologies where user experience is directly impacted.