Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Aref Einizade; Sepide Hajipour Sardouie
Volume 14, Issue 3 , October 2020, , Pages 221-233
Abstract
The brain electrical signal has been widely used in clinical and academic research, due to its ease of recording, non-invasiveness, and precision. One of the applications can be emotion recognition from the brain's electrical signal. Generally, two types of parameters (Valence and Arousal) are used to ...
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The brain electrical signal has been widely used in clinical and academic research, due to its ease of recording, non-invasiveness, and precision. One of the applications can be emotion recognition from the brain's electrical signal. Generally, two types of parameters (Valence and Arousal) are used to determine the type of emotion which in turn indicate "positive or negative" and "level of extroversion or excitement" for a specific emotion. The significance of emotion is determined by the effects of this phenomenon on daily tasks, especially in cases where the person is confronted with activities that require careful attention and concentration. In the emotion recognition problem, firstly, using proper emotion stimuli, different emotions are created for the subjects under study and the brain signals corresponding to each stimulus are recorded. The two main steps for solving the emotion recognition problem are extracting suitable features and using appropriate classification or regression methods. In previous studies, different visual and auditory have been used and various linear and nonlinear features and classifiers have been investigated. In this paper, the main goal was the improvement of linear regression algorithms to estimate the criteria for recognizing human emotions more efficiently. For this purpose we proposed a new algorithm that uses the sparseness of the mixing vector along with the linear regression cost function. The effectiveness of the proposed algorithm on simulated data has been investigated and its superiority to linear regression algorithms such as PLS, LASSO, SOPLS and Ridge was shown. Also, to apply the proposed algorithm on EEG data corresponding to emotion recognition, the DEAP dataset was used and the AR coefficients were extracted from the EEG signals. The results obtained from the proposed algorithm were compared with those of the other linear regression algorithms, which in total showed the relative superiority of the proposed method.
Biological Computer Modeling / Biological Computer Simulation
Somayeh Raiesdana
Volume 12, Issue 3 , November 2018, , Pages 249-263
Abstract
Sleep is an essential process to maintain and improve human activities, while many details related to sleep are still not well understood. Decreased or fragmented sleep is a health risk that might result in heart disease or diabetes on one hand and degradation of consciousness and cognition on the other ...
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Sleep is an essential process to maintain and improve human activities, while many details related to sleep are still not well understood. Decreased or fragmented sleep is a health risk that might result in heart disease or diabetes on one hand and degradation of consciousness and cognition on the other hand. Sleep fragmentation is a phenomenon in which an individual's sleep is intermittently disrupted by arousal caused by external factors (noise) or internal factors (apnea) although sleep deprivation does not completely occur. Computational modeling is a suitable framework for understanding complex biological mechanisms. In this paper, the fundamental phenomena underlying the sleep-wake transition was reviewed and simulated. The dynamical behavior of model was then investigated and afterwards the factors that might cause obstructive sleep apnea were implemented and evaluated. The model includes two main neuronal populations: the ascending arousal system in the brain stem that is responsible for awakening and a neuronal population in the hypothalamus, called VLPO, which mediates sleep. These populations have mutual inhibition on each other causing a flip-flop or switching behavior between sleep and wake. The results of modeling in this paper showed hysteresis in the sleep-wake cycle, the size of which is affected by factors causing arousal. In OSA, intermittent and unstable transitions as well as the shrinking of bistable zone is expected. The model could reproduce some experimental results related to obstructive apneas.
