A novel wearable ERP-based BCI approach to explicate hunger necessity.

This study aimed to design a Brain-Computer Interface system to detect people's hunger status. EEG signals were recorded in various scenarios to create a database. We extracted the time-domain and frequency-domain features from these signals and applied them to the inputs of various Machine Learning algorithms.

Physiological parameters as a tool in the diagnosis of fibromyalgia syndrome in females: A preliminary study.

Aims: Although fibromyalgia (FM) syndrome is associated with many symptoms, there is as yet no specific finding or laboratory test diagnostic of this syndrome. The physical examination and laboratory tests may be helpful in figuring out this syndrome.

Materials And Methods: The heart rate, respiration rate, body temperature (TEMP), height, body weight, hemoglobin level, erythrocyte sedimentation rate, white blood cell count, platelet count (PLT), rheumatoid factor and C-reactive protein levels and electrocardiograms (ECG) of FM patients were compared with those of control individuals.

Investigation of the relationship between anxiety and heart rate variability in fibromyalgia: A new quantitative approach to evaluate anxiety level in fibromyalgia syndrome.

Background: Fibromyalgia syndrome (FMS) is identified by widespread musculoskeletal pain, sleep disturbance, nonrestorative sleep, fatigue, morning stiffness and anxiety. Anxiety is very common in Fibromyalgia and generally leads to a misdiagnosis. Self-rated Beck Anxiety Inventory (BAI) and doctor-rated Hamilton Anxiety Inventory (HAM-A) are frequently used by specialists to determine anxiety that accompanies fibromyalgia.

Determination of Fatigue Following Maximal Loaded Treadmill Exercise by Using Wavelet Packet Transform Analysis and MLPNN from MMG-EMG Data Combinations.

The muscle fatigue can be expressed as decrease in maximal voluntary force generating capacity of the neuromuscular system as a result of peripheral changes at the level of the muscle, and also failure of the central nervous system to drive the motoneurons adequately. In this study, a muscle fatigue detection method based on frequency spectrum of electromyogram (EMG) and mechanomyogram (MMG) has been presented. The EMG and MMG data were obtained from 31 healthy, recreationally active men at the onset, and following exercise.