My dream of further research on mind controlled computing materializes, as I see these types of research results. A great read!

Two years into the program and I am having my confirmation of candidature seminar on August 7, 2013!! Both excited and anxious to be defending my confirmation for the school of Information Systems panel and advisory. Here is the abstract, if interested… 

Keywords: affective information retrieval, affective search, neuro-information science, web search performance, affective information behavior, EEG in information retrieval, emotional design

ABSTRACT

In the past decade, the affective component of information retrieval system design has increasingly become an essential part of research in information retrieval. Expressions such as “pleasurable engineering” or “emotional design” have become the driving factors in information design, where these expressions have also been extended to information retrieval system design (Nahl & Bilal, 2007). These emerging expressions indicate the important role of emotions in human-computer-interaction.

Information retrieval processes entail complicated cognitive processes. These sophisticated processes are composed of not only human cognitive processes but also human emotion responses (Picard, 2001) where these responses entail physiological as well as neurological reactions. In order to understand the role of affective responses in information retrieval, more specifically within search process, researchers need to investigate these interactions from multiple perspectives (Scherer, 2005).  However, our understanding of how emotions affect information retrieval, as revealed in search performance, is limited (Nahl & Bilal, 2007).

There is a gap in the current body of knowledge on the effect of physiological and neurological emotion responses on information retrieval, more specifically on web search processes and performance. This research aims to examine causal relationship, if any, between dimensions of human emotions and web search performance. Specifically, I intend to contribute to affective web search studies by applying emerging and cutting edge research technologies in the field of neuro-information science (Gwizdka, 2012)—such as electroencephalography (EEG)—thereby increasing our understanding of affective search and improving information systems design practices. By addressing this gap, I intend to make a significant contribution towards the specific fields of affective search and neuro-information science. 

In an attempt to investigate the data validity of one of the methods that I use in my PhD dissertation in Information Science, it has become apparent to me that a lack of methodology standards when conducting EEG (Electroencephalography) is the largest contributor to its biased reputation in this field. Here, as the advocate of use of EEG methodology in the field of Information Science, I (briefly) present an overview and argue how this method may be used as a standardized method in the field, as well as in examining various dimensions of cognitive processes.

Recording Brain’s Electrical Signals – EEG

Let me first give a quick overview of EEG brain’s electrical signals and the quantitative aspect of this method. Devices that collect EEG signals, in actuality collect the voltage fluctuations of the ionic current flow changes of the neurons of the brain (Niedermeyer & da Silva, 2004). These fluctuations of the brain signals are divided into six different wave patterns, depending on the frequency. Brain electrical signals are divided into five different brainwave types: delta, theta, alpha, low beta, midrange beta, high beta, and gamma waves. Each of these brainwave levels has its own specific frequency, ranging from 0 to 100 Hz.

The International 10-20 EEG System

The 10-20 system is a well-recognized technique that indicates specific and standardized locations of the scalp for EEG types of experiments (Niedermeyer & da Silva, 2004). The standardized locations on the scalp suggest areas on the scalp where the EEG electrodes can be set. These locations correlate with specific areas on the neocortex.

Fast Fourier Transformation (FFT)

EEG signals, through a series of mathematical functions and filters may be decomposed. Jean Baptiste Fourier (1768-1830), by developing frequency analysis, has contributed to algorithms, such as FFT, which converts time and space to frequencies (and vise versa). One of the most used quantitative measures of EEG signals is through FFT analysis. Contemporary EEG devices, such as EPOC Emotiv neuroheadset, heavily utilize FFT in order to ‘translate’ brain signals into wave lines displayed digitally and visible to the eyes.

I have covered these topics in great detail in the confirmation part my dissertation, which I hopefully get to publish soon!

Observer methodologies used in the field of information retrieval, mainly focused on user behavior and search performance – part of the paper that, currently, I am working on…

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Facial Expression

Facial expression is one of the main ways in which humans communicate emotional states with one another (Ekman & Friesen, 1975). In 1999, Paul Ekman developed a classification system that recognized the established six basic emotions (fear, happiness, surprise, anger, sadness, and disgust) of the Darwinian discrete approach (Darwin, 1872).  In addition, Ekman’s contribution to developing Facial Action Coding System (FACS) includes extraction of various facial cues that successfully helps analyze emotional states. These facial expression analysis consist of monitoring facial skin movements as well as facial features, such as in eyebrows, changes.

Most recently, facial expression reading has become a popular methodology in web search behavior studies. Arapakis (2009), for example, applied automatic facial analysis when examining the relationship between behavior and task difficulty in online search. Moreover, when studying user responses, researchers study these responses during Google search by utilizing facial analysis (Lopatovska, 2009).

Although FACS is said to have high reading validity, is non-obtrusive, and has high accuracy, (Cohn & Kanade, 2006), it does have its own limitations. Since FACS solely focuses on facial expression, it fails to take human body movements, in addition to the face, which play a big role in communication into account  (Fasel & Luettin, 2003). In adittion, FACS is unable to include the context in which user reactions occur. (Pantic & Rothkrantz, 2003).

Body Movement

There are many debates as to whether studying body movements is valid indicative of human emotions and behavior. However, some studies have been able to show clear connection between certain behavior and certain body gestures (Boone & Cunningham, 1998). These findings directly relate to the Darwinian theory, where some body movements directly relate to specific emotional states (Wallbott, 1998).  Furthermore, Gunes and Piccardi (2007) identify six facial and body gestures, connecting them with various body movements.

More specifically, researchers have monitored hand movements in hope to establish correlations between emotions and these movements. In these studies, researchers either study glove movements of the users or they have computer programs observe the movements. While glove-based studies analysis the hand gestures using model-based techniques, the vision-based approach observes hands in images using appearance-based techniques (Chen et al., 2003).

Some of the major limitations of this approach lies in difficulties analyzing the hand movements in an unbiased and controlled environment (McAllister et al., 2002).

Verbal Communication

A considerable amount of studies on verbal communication have led into a number of models in vocal communication. Lens perception model, developed by Brunswik in mid 20^th century, is said to be one of the models that helps explain the transfer of speaker’s emotional state through certain acoustic characteristics of their voice (Miroff, 1977). These characteristics usually are the pitch, speech rate, and the intensity of the voice (Pantic & Rothkrantz, 2003).

For example, over-stressing certain words communicates the variations in the pitch and the intensity (Cowie et al., 2001). Emotion of anger tends to increase blood pressure, which in turn increases the intensity of the speech (Breazeal, 2001). Voice assessment system and programs do also carry their own limitations. Data quality and capturing noise-free data, limited voice classification, and the context-independency of the audio are to name a few (Jaimes & Sebe, 2007).

Interactive Behavior

Computer log files have increasingly become a popular method in the filed of Information Retrieval for collecting information on user behavior (Kapoor et al., 2007). Data, such as number of visited pages or the time it took to find specific web resources, are some of the data logs captured and analyzed by researchers to infer frustration of users, for example. However, interactive behavior methodology still has long ways to go before it can establish relationships between behavior and search performance.

The Zurb Crew did it again! During christmas holidays, I received a cute little package from them. Not knowing what to expect, I opened it with anticipation. The package contained two items; an elegant simple Zurb Christmas card, called ‘Rock-Paper-Scissors: Best Two out of Three’ and stack of cards.

The Christmas card suggested me having friendly matches of Rock Paper Scissors with friends or on ZURBword.com! More peculiarly, the stack of cards, drew my full attention. Each card had a colorful beautiful image of a hand, painted in delightful colors, showing hand-gestures of the good-old game of Rock Paper Scissors. As a UX researcher, what was interesting to me was the fact there were no rules or guidelines following the stack of cards. It was as if the users were supposed to figure things out by themselves.

Naturally, and as a user-oriented individual, immediately I started thinking of ways in which users would think and go about playing the cards. I thought of rules, such as ways to distribute the cards among players, the order of players, the number of sets, incentives, and rewards. So, you can imagine how my thoughts started whirling around the idea…

And then it darned me; the beauty of this idea was due to several other reasons:

• This game was a mirror of an already-existing ‘good-old-street-game’ that simply contained no rules and was already familiar to the users. The user mental model was already established!
• The innovative colorful presentation of the game made it refreshing, adding additional elements of enthusiasm and fun to an old forgotten game
• Simple beautiful UIs, can liven up old and forgotten elements in our lives

Here, the Zurb Crew, elegantly, brought one of my favorite (but forgotten) childhood street-games back into my everyday living. And they achieved this by presenting an old idea with simple, yet compelling, beautiful UI. This, I call art!