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technological point of view, Donoghue [99] claimed that the foremost diffi-

culty is stable and reliable long-term recording. Furthermore, Maiseli et al. [2]

draw attention to possible threats in the application of BCI, including medical

safety, privacy, ethics, and security.

In many fields of EEG-based BCI, maybe the major challenge is the indi-

vidual differences in the EEG signal of the subjects. In general, ML approaches

work under the assumption that the feature space of a sample data set should

somewhat represent the overall characteristics and variabilities of all poten-

tial data. However, with BCI, this assumption often holds only on a subject-

specific basis. Thus, a single model would not guarantee the same consistency

for all other subjects. This cross-subject variability of brain signals poses a

unique challenge for creating subject-independent models that are universally

applicable to everyone. Adding to the cross-subject variability, there is also

the factor of temporal variability (cross-session variability), where an individ-

ual’s brain signal patterns may change over time due to factors present such

as mood, fatigue, or even the time of day. Many transfer learning and EEG

analysis studies approach this problem in a variety of innovative ways [4].

In the development of BCI applications, achieving high signal quality and

accuracy in EEG recordings remains a persistent challenge that is also caused

by environmental noise, electrode placement variability in addition to individ-

ual differences. However, ongoing developments in electrode technology, such

as dry and flexible electrodes, can address issues related to comfort, variability,

and signal quality.

BCI technology is still in its early stages despite notable advancements

in recent years. In the not-too-distant future, new solutions for all kinds of

challenges in any application field will be produced by the researchers as com-

puting resources and sensor technologies are enhanced.

7.6

Conclusion

This chapter provides an overview of the current landscape of EEG-based BCI

systems in the context of neuropsychiatric diseases. It is evident that EEG-

based BCI systems hold promise not only in unraveling the complexities of

neural patterns but also in transforming the way we diagnose, treat, and sup-

port individuals facing these challenges. The road ahead involves addressing

methodological, technological, and ethical considerations to fully harness the

capabilities of EEG-based BCIs in clinical settings.