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sensors:galvanic_skin_response [2018/11/01 18:55] – external edit 127.0.0.1 | sensors:galvanic_skin_response [2021/03/24 15:39] (current) – [Available Sensors & Specifications] charles.reimer | ||
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- | ====== Galvanic Skin Response (GSR) ====== | + | ====== |
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===== Summary ===== | ===== Summary ===== | ||
- | **Galvanic skin response** (or **GSR**), also known as // | ||
- | GSR is conducted by attaching | + | **Electrodermal activity (EDA)** is a type of biosignal (electrical signal produced in the body) which refers to the variable electrical characteristics of the skin. EDA is now the standard umbrella term for what has historically been known as **galvanic skin response (GSR)**, **electrodermal response (EDR)**, **psychogalvanic reflex (PGR)**, **skin conductance response (SCR)**, **sympathetic skin response (SSR)**, **skin conductance level (SCL)**, and **skin conductance (SC)**. This article will favour the use of the term EDA. |
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+ | EDA is an easy-to-measure and generally reliable measurement commonly used in psychophysiological studies. The signal is sensitive to changes in mental state and cognitive processes. It has the advantages of being convenient and economical, and can be applied in both laboratory and real-world settings. EDA is commonly used in psychophysiological studies to measure activations of the sympathetic nervous system, emotional arousal, and cognitive phenomena such as stress and cognitive load. It should be noted that there is is not absolute agreement on exactly what EDA measures psychophysiologically, | ||
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+ | EDA measurements are often combined with other physiological measurements such as blood pressure, heart rate, and respiration rate. This is an important consideration as EDA responses are often but one of many psychophysical phenomena occurring in response to activation of the autonomic nervous system. | ||
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+ | ===== Origins, Theory, & Physiology ===== | ||
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+ | EDA measurement has a long history, dating back to experiments | ||
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+ | While the exact origins and physiological mechanisms of EDA are complex and still being investigated, | ||
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+ | Traditional EDA theory links changes in electrical resistance of the skin with sweat glands controlled by the sympathetic branch of the autonomic nervous system (responsible for many of the body’s physiological responses). Increased activation of the sympathetic nervous system | ||
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+ | It should be noted that different areas of skin on the human body display different electrical characteristics due to the distribution of sweat glands. Different types of sweat glands exist as well, although eccrine sweat glands are of primary interest in EDA, as this type of gland is closely related to psychological stimulation. Non-eccrine sweat glands are more closely related to temperature regulation. Eccrine glands are most prevalent in the hands and feet. | ||
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+ | ===== Electrodermal Measurement Principles & Data Components ===== | ||
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+ | Measurement of EDA is based on the fundamental electrical principle of **[[https:// | ||
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+ | Skin electrical | ||
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+ | **Endosomatic methods** measure potential differences at various points on the skin surface without application of external electrical current. **Endosomatic methods** are not commonly used, given that they often produce bipolar signals that are complex waveforms, rendering the measurements taken difficult to score and interpret. | ||
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+ | **Exosomatic methods** use an external electrical source to pass a small alternating current (AC) or direct current (DC) through the skin to measure the electric resistance to this current. The most commonly used method | ||
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+ | EDA data consists of two primary components: **Tonic Skin Conductance Level (SCL)** and **Phasic Skin Conductance Response (SCR)**. | ||
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+ | **SCL** shows patterns of slow variation over tens of seconds to minutes, and is constantly changing based on individuals’ hydration, skin moisture, and autonomic regulation. **SCL** varies notably across individuals, | ||
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+ | **SCR** alternates more quickly, showing characteristic EDA bursts or peaks (**Event-Related SCR**; **ER-SCR**), | ||
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+ | {{: | ||
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+ | //Typical electrodermal measures, definitions, | ||
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+ | {{: | ||
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+ | //Phasic & Tonic Conductance Components [[https:// | ||
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+ | ===== Measuring EDA ===== | ||
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+ | EDA is measured using two electrodes placed on the surface of the skin, often in the form of a patch sticker (which requires conductive gel) or embedded within a velcro strap. Electrodes are often made of Silver/ | ||
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+ | EDA sensors typically include two electrodes, an amplifier to increase signal amplitude, and an analog-to-digital converter. Wireless sensor systems will also contain modules for data transmission. Various sensors exist with different technical specifications, | ||
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+ | {{: | ||
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+ | //EDA measurement using [[https:// | ||
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+ | EDA sensors are often combined with other biometric sensors such as eye tracking, facial expression analysis, electroencephalography, | ||
+ | |||
+ | ===== EDA Sensor Setup & Calibration ===== | ||
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+ | The areas of skin most responsive to emotional stimuli are good candidates for EDA measurement electrode placement. These areas include the fingers, the palms of the hands, and the soles of the feet. In a study of EDA electrode placement on different parts of the body, [[https:// | ||
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+ | When placing electrodes on the fingers, it is common to take measurements | ||
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+ | {{: | ||
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+ | //Electrode placement for palm and fingers [[https:// | ||
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+ | {{: | ||
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+ | //Electrode placement for the sole of the foot [[https:// | ||
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+ | It can also be useful to treat the skin area where the electrodes are to be applied. In cases of oily skin, it can be useful to use 70% isopropanol for cleaning to optimize sensor stability. In cases of very dry skin, adding skin moisturizer can be beneficial. | ||
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+ | During setup, a baseline period of two to four minutes should be included at the beginning of data recording to allow identification of issues related to skin moisture and environmental conditions, and to establish | ||
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+ | In order to avoid muscular artifacts, individuals should breathe normally, minimize unnecessary limb movements, and avoid talking. They should be seated | ||
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+ | For circumstances in which EDA measurement quality is adversely affected by vigorous movement, [[https:// | ||
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+ | ===== Sensor Use Considerations ===== | ||
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+ | [[https:// | ||
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+ | EDA measurements can be affected by a number of technical, environmental, | ||
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+ | Technical considerations relate to sample rate, sources of electrical noise, and whether or not a system | ||
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+ | Very low sampling rates (1 to 10 Hz) are often sufficient for EDA measurement, | ||
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+ | In most electrode-based biosignal interfaces, stray capacitance and stray inductance from a number of sources can cause undesirable electrical noise in the interface output. Extraneous noise due to power mains interference can be reduced | ||
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+ | When using wireless sensing systems, it is essential to ensure that the transmitter and receiver are within the recommended range for the entire duration of measurement. Bluetooth signals can also be occluded by water, concrete, or human tissue. | ||
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+ | Since EDA bursts typically occur with a delay of 1 to 5 second from the stimulus presentation, | ||
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+ | Individual differences in skin response are also important to consider, as individuals experience psychological phenomena differently, | ||
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+ | Habituation is another important consideration when taking EDA measurements. This describes the phenomena of declining responsiveness to familiar or non-significant stimuli presentations over time. A number of methods exist for quantifying habituation. | ||
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+ | Some individuals possess a trait known as electrodermal lability, which refers to a high rate of non-specific skin conductance response and/or slow habituation. This trait tends to be reliable over time, and has been shown to be present in populations with schizophrenia, | ||
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+ | ===== Output & Data Analysis ===== | ||
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+ | Electrodermal activity is commonly | ||
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+ | Generally, response peaks are of primary interest, as these typically occur in response to external stimulation. Latency refers to the time elapsed between stimulus onset and the beginning of the EDA peak, while rise time refers to the duration between peak onset and maximum amplitude. Recovery time describes the time elapsed between the maximum peak amplitude and the return to baseline. Peak magnitude/ | ||
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+ | More complex data can also be calculated and examined, including number of response peaks, peak amplitude, rise duration, peak area, accumulative EDA, and frequency power. | ||
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+ | {{: | ||
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+ | //EDA response peak components [[https:// | ||
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+ | ===== Signal Processing ===== | ||
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+ | Physiological data typically requires computational procedures and/or machine learning techniques for meaningful pattern extraction. A number of techniques are used when working with raw EDA data. | ||
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+ | Since many EDA measurements are taken at a higher sample rate than required, downsampling is typically used to lower the sample rate. Data loss is not a significant risk when downsampling in this case. | ||
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+ | When measuring biosignals, there is typically a need to remove noise from a measurement in order to increase the signal-to-noise ratio. For EDA, a basic median filter can be used to smooth the EDA data and remove the tonic component of the signal which is unrelated to the stimulus-response peaks. | ||
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+ | Automatic detection algorithms can be used to identify peak amplitudes, onsets, and offsets using various thresholds. | ||
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+ | Various software solutions exist to collect process EDA data, including [[https:// | ||
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+ | ===== Applications & Uses ===== | ||
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+ | As mentioned in previous sections, EDA measurement is very common in psychophysical research, and is often used in tandem with other biosignal measures. Additionally, | ||
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+ | Within psychophysical research domains, EDA measurement techniques are utilized within paradigms that present discrete stimuli (such as emotionally arousing images) and evaluate EDA response, as well as in paradigms that examine continuous skin response over time when engaging with a continuous stimulus, such as a video game. Measurements of EDA have been used to investigate stimulus-related psychophysiological phenomena, such as orienting, defensive, and startle responses, responses to affective and novel stimuli, habituation, | ||
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+ | More practical applications of EDA sensors include measurement of skin response in populations with anxiety, depression, schizophrenia, | ||
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+ | In engineering psychology, human-computer interaction, | ||
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+ | Medical applications include the use of EDA measurements in dermatology—to assess skin pathology—and neurology—to assess damage to the central and peripheral nervous systems, such as brain lesions, degenerative diseases, and neuropsychological disorders such as agnosia. | ||
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+ | The use of EDA measures and other biosensors can allow for non-invasive measurement of individuals of cognitive and affective states while not interrupting the performance of other tasks. This can allow for the development of affective computing systems that dynamically respond to users’ cognitive and affective state in real time. In many cases, feature extraction alone may be all that is required from EDA data; however, machine learning algorithms are useful when considering systems that need to adapt and respond to changing EDA measurements in real-time. | ||
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+ | One notable affective computing application is [[https:// | ||
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+ | EDA sensors have been used in number of musical applications. One notable example | ||
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+ | [[http:// | ||
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+ | EDA sensors were also used in [[https:// | ||
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+ | With the rise of wearable technology, EDA sensors are also becoming more prevalent in fitness and wellness technologies, | ||
+ | |||
+ | ===== Further Reading ===== | ||
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+ | For those seeking a more detailed resource on this topic, [[https:// | ||
+ | |||
+ | ===== Available Sensors & Specifications ===== | ||
+ | |||
+ | This section lists a variety of EDA sensors from consumer electronics to open-source hardware to medical grade sensors. The best information on how to use each particular sensor can be found within the linked manuals and documentation. | ||
+ | |||
+ | {{template> | ||
+ | |company=BioPac | ||
+ | |model=SS57LA | ||
+ | |sources=[[https:// | ||
+ | |description=EDA Electrode Leads | ||
+ | |datasheet=None | ||
+ | |resources=[[https:// | ||
+ | |notes=Requires separate Biopac data acquisition and analysis hardware (much as the MP36, MP36R, or MP160 systems) compatible with Windows operating systems. The MP160 system also requires a separate electrodermal activity amplifier (EDA100C or EDA100D). Can be wireless using either the BioNomadic Logger or MP160 system). Biopac systems also include Biopac Student Lab (for the MP36 system) or AcqKnowledge (for the MP160 and MP36R systems) | ||
+ | |variants=SS57L | ||
+ | }} | ||
+ | |||
+ | {{template> | ||
+ | |company=Biosignalsplux | ||
+ | |model=Electrodermal Activity (EDA) | ||
+ | |sources=[[https:// | ||
+ | |description=EDA Electrode Leads | ||
+ | |datasheet=[[https:// | ||
+ | |resources=[[https:// | ||
+ | |notes=Requires biosignalsplux hub and electrodes for use. Can be used with OpenSignals (R)evoltion software (Windows/ | ||
+ | |variants=None | ||
+ | }} | ||
+ | |||
+ | {{template> | ||
+ | |company=BITalino | ||
+ | |model=EDA Sensor | ||
+ | |sources=[[https:// | ||
+ | |description=Arduino-compatible EDA sensor | ||
+ | |datasheet=[[https:// | ||
+ | |resources=[[https:// | ||
+ | |notes=None | ||
+ | |variants=None | ||
+ | }} | ||
+ | |||
+ | {{template> | ||
+ | |company=Empatica | ||
+ | |model=E4 | ||
+ | |sources=[[https:// | ||
+ | |description=Wearable bracelet with sensors for EDA and other physiological phenomena | ||
+ | |datasheet=[[https:// | ||
+ | |resources=[[https:// | ||
+ | |notes=Wireless (Bluetooth). Can be used with the E4 Realtime mobile application or the E4 Manager desktop application. | ||
+ | |variants=Empatica EmbracePlus | ||
+ | }} | ||
+ | |||
+ | {{template> | ||
+ | |company=Infusion Systems | ||
+ | |model=BioEmo | ||
+ | |sources=[[https:// | ||
+ | |description=GSR impedance sensor | ||
+ | |datasheet=Technical Specifications can be found at [[https:// | ||
+ | |resources=[[https:// | ||
+ | |notes=Wireless. Requires Infusion Systems I-CubeX Digitizer. | ||
+ | |variants=None | ||
+ | }} | ||
+ | |||
+ | {{template> | ||
+ | |company=Mindfield Biosystems | ||
+ | |model=eSense Skin Response | ||
+ | |sources=[[https:// | ||
+ | |description=Skin conductance sensor which can send data through phone/ | ||
+ | |datasheet=See [[https:// | ||
+ | |resources=[[https:// | ||
+ | |notes=Can be used with the Mindfield Biosystems eSense application (iOS/ | ||
+ | |variants=None | ||
+ | }} | ||
+ | |||
+ | {{template> | ||
+ | |company=Movisens | ||
+ | |model=EdaMove 4 | ||
+ | |sources=[[https:// | ||
+ | |description=Bluetooth sensor for collecting various physiological data | ||
+ | |datasheet=[[https:// | ||
+ | |resources=[[https:// | ||
+ | |notes=Wireless (Bluetooth). Can be used with Movisens SensorManager/ | ||
+ | |variants=EdaMove3 | ||
+ | }} | ||
+ | |||
+ | {{template> | ||
+ | |company=Seeed Studio | ||
+ | |model=GSR Sensor | ||
+ | |sources=[[https:// | ||
+ | |description=Arduino-/ | ||
+ | |datasheet=Brief technical specifications are available at [[https:// | ||
+ | |resources=[[https:// | ||
+ | |notes=Arduino/ | ||
+ | |variants=None | ||
+ | }} | ||
+ | |||
+ | {{template> | ||
+ | |company=Shimmer Sensing | ||
+ | |model=Shimmer3 EDA+ | ||
+ | |sources=[[https:// | ||
+ | |description=Wireless GSR and pulse-sensing system | ||
+ | |datasheet=[[https:// | ||
+ | |resources=[[https:// | ||
+ | |notes=Wireless (Bluetooth). No proprietary connectors. Open system also compatible with Shimmer software such as Consensys. | ||
+ | |variants=None | ||
+ | }} | ||
===== References ===== | ===== References ===== | ||
- | * [[wp> | + | * BIOPAC Systems Inc. (2021). //Skin Conductance Response Analysis// |
- | * [[http://infusionsystems.com/catalog/product_info.php/products_id/203|I-CubeX BioEmo | + | application/ |
+ | |||
+ | * Boucsein, W. (2012). // | ||
+ | |||
+ | * Braithwaite, | ||
+ | |||
+ | * Dawson, M. E., Schell, A. M., & Filion, D. L. (2016). The electrodermal system. In Cacioppo, J. T., Tassinary, L. G., Berntson, G. G. (Eds.), // | ||
+ | |||
+ | * Edelberg, R. & Burch, N. R. (1962). Skin resistance and galvanic skin response. //Archives of General Psychiatry, 7//(3), 163-169. [[https:// | ||
+ | |||
+ | * Electrodermal activity (2021, February 13). In Wikipedia. [[https:// | ||
+ | |||
+ | * Essl, G. & Won Lee, S. (2018). Mobile devices as musical instruments - State of the art and future prospects. In // | ||
+ | |||
+ | * Healey, J. & Picard, R. W. (1998). StartleCam: A cybernetic wearable camera. In //Digest of Papers, International Symposium on Wearable Computers// | ||
+ | |||
+ | * iMotions (2017). //Galvanic Skin Response: The Complete Pocket Guide//. [[https:// | ||
+ | |||
+ | * Knapp, R. B., & Bortz, B. (2011). MobileMuse: Integral music control goes mobile. In //Proceedings of the International Conference on New Interfaces for Musical Expression// | ||
+ | |||
+ | * Marrin Nakra, T. (2000). Searching for meaning in gestural data: Interpretive feature extraction and signal processing for affective and expressive content. In Wanderley, M. M. & Battier, M. (Eds.). //Trends in Gestural Control of Music// (pp. 415-438). Paris: IRCAM, Centre Pompidou | ||
+ | |||
+ | * Miranda, E. R. & Wanderley, M. M. (2006). Biosignal interfaces. In Miranda, E. R. & Wanderley, M. M. (Eds.), //New Digital Musical Instruments: | ||
+ | |||
+ | * Müller, A., Fuchs, J., & Röpke, K. (2011). Skintimacy: Exploring interpersonal boundaries through musical interactions. In // | ||
+ | |||
+ | * Nourbakhsh, N., Chen, F., Wang, Y., & Calvo, R. A. (2017). Detecting users’ cognitive load by galvanic | ||
+ | |||
+ | * Picard, R. W. (1997).// Affective Computing// | ||
+ | |||
+ | * Sharma, M., Kacker, S., & Sharma, M. (2016). A brief introduction and review on galvanic skin response. // | ||
+ | |||
+ | * Van Dooren, M., de Vries, J. J. G., & Janssen, J. H. (2012). Emotional sweating across the body: Comparing 16 different skin conductance measurement locations. // | ||
+ | |||
+ | * Westyn, T., Presti, P., & Starner, T. (2006). ActionGSR: A combination galvanic skin response-accelerometer for physiological measurements in active environments. In // | ||
+ | |||
+ | * Yuksel, B. F., Oleson, K. B., Chang, R., & Jacob, R. J. K. (2019). Detecting and adapting to users’ cognitive and affective state to develop intelligent musical interfaces. In Holland, S., Mudd, T., Wilkie-McKenna, | ||
+ | |||
+ | * Zhou, F. & Jianxin Jiao, R. (2013). Eliciting, measuring, and predicting affect via physiological measures for emotional design. In Fukuda, S. (Eds.), //Emotional Engineering vol. 2// (41-62). Springer, London. [[https:// | ||
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