Neural interface, sometimes referred to as brain-computer interface (BCI) or brain-machine interface (BMI), is an emerging interdisciplinary technology to decode various human intentions and emotions from neural signals. Traditionally, the neural interface technology has been developed with the aim to provide paralyzed people with new channels of communication. Recently, thanks to the rapid development of portable neural signal recording devices, the neural interface technology is extending its application fields into consumer electronics, entertainment (neuroentertainment), and marketing (neuromarketing).
Neuroarchitecture can be regarded as a branch of neuromarketing, defined as a new field of marketing research that studies consumer’s sensorimotor, cognitive, and affective response to marketing stimuli (a definition from Wikipedia), if buildings and structures are assumed to be sorts of products.
Electroencephalogram (EEG), a type of biosignals generated by the neural electrical activity, has been one of the major tools to study neuromarketing as it has much higher temporal resolution than functional magnetic resonance imaging (fMRI). EEG can also be used as a powerful tool to study neuroarchitecture, especially when wearable and portable EEG devices are used. Although more than 30 wearable type EEG devices are currently available in market, further studies still need to be performed for the wearable neural interface devices to be more reliably used for the neuroarchitecture studies.
Most of the currently available portable EEG devices have a few electrodes covering only prefrontal area of the brain. Since these types of portable EEG recording devices are not only inherently prone to eye blink or eye movement artifacts but also generally does not have an electrooculogram channels that can be used as a useful artifact indicator, the recorded EEG signals can be severely distorted. Moreover, the information attainable from these devices is generally restricted due to their small numbers of channels. To circumvent this issue, new EEG devices with multiple dry type active electrodes that can cover whole scalp surface without a need for using electrode gel have been being developed.
The other issue that also needs to be addressed in future studies is the large inter-individual variability of EEG features (e.g., spectral power of a specific frequency band, frontal alpha asymmetry) having been frequently used for the identification of individual subject’s cognitive or affective states. Due to such large individual variability, it is generally difficult to develop a “universal classifier” that can be applied to “all the users” without any customization or individualization process. Many research groups including us are trying to develop new algorithms and approaches to circumvent this issue. Combined use of multiple physiological data such as photoplethysmogram (PPG), Galvanic skin response (GSR), and body temperature change might also shed light on this issue.