title: Electro-physiological data fusion for stress detection.
abstract: In this work we describe the performance evaluation of a system for stress detection. The analysed data is acquired by following an experimental protocol designed to induce cognitive stress to the subjects. The experimental set-up included the recording of electroencephalography (EEG) and facial (corrugator and zygomatic) electromyography (EMG). In a preliminary analysis we are able to correlate EEG features (alpha asymmetry and alpha/beta ratio using only 3 channels) with the stress level of the subjects statistically (by using averages over subjects) but also on a subject-to-subject basis by using computational intelligence techniques reaching classification rates up to 79% when classifying 3 minutes takes. On a second step, we apply fusion techniques to the overall multi-modal feature set fusing the formerly mentioned EEG features with EMG energy. We show that the results improve significantly providing a more robust stress index every second. Given the achieved performance the system described in this work can be successfully applied for stress therapy when combined with virtual reality.
authors.affiliation: Starlab Barcelona, Spain. alejandro.riera@starlab.es
authors.forename: Alejandro
authors.initials: A
authors.lastname: Riera
authors.forename: Aureli
authors.initials: A
authors.lastname: Soria-Frisch
authors.forename: Anton
authors.initials: A
authors.lastname: Albajes-Eizagirre
authors.forename: Pietro
authors.initials: P
authors.lastname: Cipresso
authors.forename: Carles
authors.initials: C
authors.lastname: Grau
authors.forename: Stephen
authors.initials: S
authors.lastname: Dunne
authors.forename: Giulio
authors.initials: G
authors.lastname: Ruffini
mesh_terms: D004569:Electroencephalography / Q000295:instrumentation*; D004576:Electromyography / Q000295:instrumentation*; D005240:Feasibility Studies; D006801:Humans; D013315:Stress, Psychological / Q000175:diagnosis* / Q000503:physiopathology; D013647:Task Performance and Analysis