The large range of potential applications, not only for patients but also for healthy people, that could be achieved by affective brain-computer interface (aBCI) makes more latent the necessity of finding a commonly accepted protocol for real-time EEG-based emotion recognition. Based on wavelet package for spectral feature extraction, attending to the nature of the EEG signal, we have specified some of the main parameters needed for the implementation of robust positive and negative emotion classification. Twelve seconds has resulted as the most appropriate sliding window size; from that, a set of 20 target frequency-location variables have been proposed as the most relevant features that carry the emotional information.
View Article and Find Full Text PDFObjective: This study aimed to determine the effectiveness of a physical therapist-designed program tailored to axillary web syndrome (AWS) in women after breast cancer surgery.
Methods: A prospective, single-center, assessor-blinded, randomized controlled trial was conducted at the Physiotherapy in Women's Health Research Unit of the Alcalá University (Madrid, Spain). Ninety-six women with AWS were assigned to the physical therapy group (manual lymph drainage [MLD] using resorption strokes and arm exercises as if performing median nerve neurodynamic glide exercises with no neural loading; n = 48) or the control group (standard arm exercises; n = 48), with both groups receiving treatment 3 times a week for 3 weeks.
Introduction: Fatigue is a frequent and disturbing symptom in oncology but remains undertreated. Given the absence of effective drug treatment, non-pharmacological interventions have a prominent place in the treatment of fatigue. However, they are relatively unknown by professionals who lack of clear points of reference to refer patients with confidence.
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