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occurrence of a stimulus and the corresponding neural response) could be eval-

uated in both analyses. Additionally, Hjorth parameters that include three

statistical parameters (activity, mobility, and complexity) [39] are another

useful analysis, especially for real-time systems due to low computation cost.

In BCI systems, the other prevailing and standard analyses could be cat-

egorized in the frequency domain. Fourier decomposition is the most widely

used method to convert the time domain to the frequency domain, result-

ing in a complex spectrum in which each frequency is represented by power

and phase. Wavelet analysis and Hilbert transformation after band-pass fil-

tering are the other common methods for the transformation to the frequency

domain. By utilizing the outputs of the analysis, multiple features could be

calculated such as mean and/or peak frequencies, mean and/or maximum

power in specific frequency bands, etc. Subsequently, frequency domain anal-

yses contain various functional connectivity metrics that are estimated to

evaluate neuronal interactions, including coherence, phase synchronization,

phase-slope index, and Granger causality [40].

On the other hand, if there is a need to assess changes in amplitude or

power spectrum over time, it is recommended to choose the time-frequency

domain analysis. This domain can include techniques like event-related spec-

tral perturbations (ERSPs) that could be obtained via different mathematical

methods (e.g., wavelet transform) focus on changes in spectral properties over

time, and inter-trial phase coherence (ITPC) that represent phase consistency

over trials (typically within the range of zero to one) [41]. The percentage of

using the time-frequency domain features is higher than the other domains

in the EEG MI-BCI systems [42]. In recent motor imagery BCI applications,

spatial domain analyses are also employed for the feature extraction step.

One of the popular algorithms in this domain is Common Spatial Patterns

(CSP) which involves converting EEG signals into a different space through

spatial filtering techniques, aiming to optimize the variance of one group while

minimizing the variance of the second group [43].

7.3.5

Artificial Intelligence Techniques in EEG-based BCI

Systems for Neural Decoding

The mid-20th century saw the birth of Artificial Intelligence (AI) as a field

of study, with early applications focusing on problem-solving and symbolic

methods. With today’s technological advancement in AI, from industries to

home appliances, it has become a ubiquitous reality that is integral to our

daily experiences.

Once the feature-extraction process has been established in a BCI system,

proper neural decoding models should be selected to recognize feature patterns

(e.g., ERP features when a paralyzed user’s intent to move a cursor using their

thoughts or oscillatory activities when an individual with cognitive decline

pay attention to external stimuli). To date, various AI techniques, including

conventional ML and DL approaches, have been developed and introduced to