Challenges & Limitations: Smart Affective Neuro Search

In my previous post, Smart Affective Neuro Search, partially I discussed my (in progress) dissertation as well as my somewhat unconventional proposal in regards to the implications this model may have in the industry. Here, I intend to examine and discuss some of the challenges and limitations of this model.

In my pilot study, I was able to test my research design along with its proposed methods and measurement techniques. One of these techniques included the Observer Methods, more specifically body movements.

Shortly, I will discuss some of the challenges and limitations of my model. But before that, let’s review the different views when it comes to observing and measuring body movements.

Body Movement – Observer Methods

Some researchers debate whether body movements are valid indicators of human emotions. However, many studies include strong evidence that associates body movements with specific emotions (Wallbott, 1988; de Meijer, 2005). Boone and

Cunningham (1998) were also able to show connection between certain emotions and certain body movements. These findings pertain to the Darwinian theory of discrete emotions, where some body movements directly relate to specific emotional states (Wallbott, 1998). Furthermore, Gunes and Piccardi (2007) identified six facial and body gestures, connecting them with various emotions (see table below).

Table. List of Bodily Emotions (Gunes & Piccardi, 2007)

This table suggests that certain emotions may be assessed by certain body movements. For example, this table suggests that hands resting on the waists or made to fists appear to be indicators of anger in an individual. Moreover, the table indicates that signs of anxiety in participants may be detected through observations of the location of their hands on the table surface.

Moreover, researchers have been monitoring hand movements in hope of establishing correlations between emotions and these hand movements. In these studies, researchers either study glove movements of the users or they have computer programs observe the movements. While glove-based studies analyze the hand gestures using model-based techniques, the computer-based approach observes hands in images using appearance-based techniques (Chen et al., 2003).

Challenges & Limitations

Although human body movements play a big role in emotional communication (Fasel & Luettin, 2003), the complex motor movements involved may contribute to major amount of ‘noise’ when it comes to the readings of brain electrical signals. The current EEG devices in the market today, are partly designed to include noise suppressions. However, they still may not be able to fully suppress all the ‘noise’ emanating from major body movements, such as head or hand movements while participants conduct search tasks on various computer devices.

These, I believe are some of the challenges and limitations when it comes to this proposed model for developing Affective Neuro Search. It is my intention to, through continuous research, address these challenges and limitations.

In future posts, and as I progress in my dissertation, I will discuss proposed ways in which raw EEG data may be best analyzed for the purpose of Affective Neuro Search and such…

Please also see here about exciting and emerging wearable computing and AI devices here: Brain controlled airplanes, neurogaming, and robots that learn behavior.