Neuroscience and emotions
In 1884, the journal Mind  published a scientific paper by William James where he proposed that the physiological and behavioral responses of the human body actually precede the subjective experience of emotions.
These physiological responses (referred to as “different expressions of the body”) show different patterns for each emotional experience. Since then, this paradigm has inspired many scientists who have focused their research on understanding the relationship between emotions and the specificity of the activity of the autonomic nervous system.
- o Structural model of emotion: There are two predominant alternatives in the structural model on which emotions are represented: the discrete model (joy, satisfaction, anxiety, fear, etc.) and the continuous valence – activation – monitoring model (valence represents the positive-negative hedonic tone, activation is the excitement level, and monitoring is the energy level) . The following figure depicts the basic emotional continuous model (valence – activation) together with an approach to its discrete correspondence .
- Characterization of physiological responses through activity measurement: The most widely used activities to characterize physiological responses are the electrodermal activity (EDA), cardiovascular activity (CVA), respiratory activity (RA) and brain activity (obtained through electroencephalogram EEG or functional magnetic resonance imaging fMRI). Numerous scientific articles support the specificity of the activity of the central nervous system in building the continuous and discrete emotional models using measurements of these activities (see recent reviews in [4, 5, 6]).
Measures which can be obtained with usenns WorkStations
The usenns ring (included both in WorkStation Mini and WorkStation Pro) is a wearable and wireless technology that measures emotions You can naturally monitor two of the most used physiological activities in emotion discrimination by researchers. More specifically, with the usenns ring you can monitor:
- Electrodermal activity (EDA): characterized by the degree of variation of electric current passing through the skin, which depends on the amount of perspiration. The measurements most used by researchers to discriminate emotions are the skin conductance level (SCL), skin conductance response rate (NsrR) and skin conductance amplitude (SCR).
- Cardiovascular activity (CVA): characterized by changes in blood flow through arteries and veins. The measurements most used by researchers to discriminate emotions are heart rate (HR), heart rate variability (HRV) and systolic and diastolic blood pressure (SBP and DBP).
The usenns ring is placed around the fingers phalanges since this is the body part where the electrodermal activity is most reliably measured. In this area there is a high density of sweat glands  and it is the recommended position by the Society for Psychophysiological Research.
In addition, given that in the majority of studies participants move, causing distortion of physiological measurements by mechanical artefacts, the usenns ring incorporates a motion sensor (3-axis accelerometer) in combination with other sensors. This innovation allows for the exact estimation of the displacement, and for the elimination of signal noise, enabling studies in natural movement conditions to be carried out reliably with no data loss.
The usenns neuroheadset (included in the WorkStation Pro) is a technology to measure emotions and cognitive processes. It is wearable and wireless allowing an easy monitoring of the brain activity (EEG).
The usenns neuroheadset is placed around the cranial perimeter and it includes bands on the frontal and parietal areas. Its sensors are placed in a way which allows to measure the brain responses on the areas related to the emotions and cognitive processes. No conducting gel needs to be applied. The usenns neuroheadset design assures the maximum reliability (with a sampling frequency of 256Hz) without giving up the comfort when wearing it as well as a fast positioning (It takes approximately 120 seconds).
 James, W., 1884. What is an emotion? Mind 9, 188-205.
 Christie, I.C., Friedman, B.H., 2004. Autonomic specificity of discrete emotion and dimensions of affective space: a multivariate approach. International Journal of Psychophysiology 51, 143-153.
 Georgios Paltoglou, Michael Thelwall, 2013. Seeing Stars of Valence and Arousal in Blog Posts, IEEE Transactions on Affective Computing, vol.4, no. 1, pp. 116-123.
 Frieman, B.H., 2010. Feelings and the body: The Jamesian perspective on autonomic specificity of emotion. Biological Psychology 84 (3), 383-393.
 Kreibig, S.D., 2010. Autonomic nervous system activity in emotion: A review. Biological Psychology 84 (3), 394-421.
 Eddie Harmon-Jones, Philip A. Gable, Carly K. Peterson, 2010. The role of asymmetric frontal cortical activity in emotion-related phenomena: A review and update. Biological Psychology 84 (3), 451-462.
 Van Dooren, M., de Vries, J.J.G., Janssen, J.H., 2012. Emotional sweating across the body: Comparing 16 different skin conductance measurement locations. Physiology & Behavior, 106(2): 298-304.
 Picard R.W., Vyzas, E., Healey, J. 2001. Toward machine emotional intelligence: Analysis of aﬀective physiological state. IEEE Transactions Pattern Analysis and Machine Intelligence, 23(10):1175–1191.
 Kim, K.H., Bang, S.W., Kim, S.R. 2004. Emotion recognition system using short-term monitoring of physiological signals. Medical & Biological Engineering & Computing, 42:419–427.
 Lisetti, C. L., Nasoz, F. 2004. Using noninvasive wearable computers to recognize human emotions from physiological signals. EURASIP Journal on Applied Signal Processing, 11:1672–1687.
 Nasoz, F., Alvarez, K., Lisetti, C. L., Finkelstein., N. 2003. Emotion recognition from physiological signals for presence technologies. International Journal of Cognition, Technology and Work, Special Issue on Presence, 6(1).