Just like a new recipe needs to be tested with different ingredients, Brain-computer interfaces (BCIs) need to be evaluated in various age groups. A recent study examined how older and younger adults respond to visual stimuli used in BCIs. The researchers analyzed the transient electrical signals in the brain, called electroencephalogram (EEG), as well as the steady-state responses. They found that older adults had higher amplitudes of a specific EEG component called P1 when responding to motion onset, potentially giving them an advantage in motion-based BCIs. Additionally, the study revealed that BCIs based on action observation, where participants watched videos of hand movements, were feasible for older adults. However, they also noted that new algorithms are necessary to improve the performance of BCIs for older adults, particularly when identifying action observation targets.

These findings have exciting implications for the development of BCIs for senior populations. They highlight the need to consider age-related differences in brain responses when designing BCIs and underscore the potential advantage of using motion-based stimuli for older adults. Furthermore, by showing the feasibility of action observation-based BCIs in seniors, this study opens up avenues for more immersive and intuitive approaches in geriatric BCI research. To delve deeper into these findings and explore the possibilities of BCIs in older adults, check out the full research article.

ObjectiveBrain-computer interface (BCI) has great potential in geriatric applications. However, most BCI studies in the literature used data from young population, and dedicated studies investigating the feasibility of BCIs among senior population are scarce. The current study, we analyzed the age-related differences in the transient electroencephalogram (EEG) response used in visual BCIs, i.e., visual evoked potential (VEP)/motion onset VEP (mVEP), and steady state-response, SSVEP/SSMVEP, between the younger group (age ranges from 22 to 30) and senior group (age ranges from 60 to 75).MethodsThe visual stimulations, including flicker, checkerboard, and action observation (AO), were designed with a periodic frequency. Videos of several hand movement, including grasping, dorsiflexion, the thumb opposition, and pinch were utilized to generate the AO stimuli. Eighteen senior and eighteen younger participants were enrolled in the experiments. Spectral-temporal characteristics of induced EEG were compared. Three EEG algorithms, canonical correlation analysis (CCA), task-related component analysis (TRCA), and extended CCA, were utilized to test the performance of the respective BCI systems.ResultsIn the transient response analysis, the motion checkerboard and AO stimuli were able to elicit prominent mVEP with a specific P1 peak and N2 valley, and the amplitudes of P1 elicited in the senior group were significantly higher than those in the younger group. In the steady-state analysis, SSVEP/SSMVEP could be clearly elicited in both groups. The CCA accuracies of SSVEPs/SSMVEPs in the senior group were slightly lower than those in the younger group in most cases. With extended CCA, the performance of both groups improved significantly. However, for AO targets, the improvement of the senior group (from 63.1 to 71.9%) was lower than that of the younger group (from 63.6 to 83.6%).ConclusionCompared with younger subjects, the amplitudes of P1 elicited by motion onset is significantly higher in the senior group, which might be a potential advantage for seniors if mVEP-based BCIs is used. This study also shows for the first time that AO-based BCI is feasible for the senior population. However, new algorithms for senior subjects, especially in identifying AO targets, are needed.

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