Adaptive multi-degree of freedom Brain Computer Interface using online feedback: Towards novel methods and metrics of mutual adaptation between humans and machines for BCI.

This paper proposes a novel adaptive online-feedback methodology for Brain Computer Interfaces (BCI). The method uses ElectroEncephaloGraphic (EEG) signals and combines motor with speech imagery to allow for tasks that involve multiple degrees of freedom (DoF). The main approach utilizes the covariance matrix descriptor as feature, and the Relevance Vector Machines (RVM) classifier. The novel contributions include, (1) a new method to select representative data to update the RVM model, and (2) an online classifier which is an adaptively-weighted mixture of RVM models to account for the users' exploration and exploitation processes during the learning phase. Instead of evaluating the subjects' performance solely based on the conventional metric of accuracy, we analyze their skill's improvement based on 3 other criteria, namely the confusion matrix's quality, the separability of the data, and their instability. After collecting calibration data for 8 minutes in the first run, 8 participants were able to control the system while receiving visual feedback in the subsequent runs. We observed significant improvement in all subjects, including two of them who fell into the BCI illiteracy category. Our proposed BCI system complements the existing approaches in several aspects. First, the co-adaptation paradigm not only adapts the classifiers, but also allows the users to actively discover their own way to use the BCI through their exploration and exploitation processes. Furthermore, the auto-calibrating system can be used immediately with a minimal calibration time. Finally, this is the first work to combine motor and speech imagery in an online feedback experiment to provide multiple DoF for BCI control applications.

Inferring imagined speech using EEG signals: a new approach using Riemannian manifold features.

OBJECTIVE: In this paper, we investigate the suitability of imagined speech for brain-computer interface (BCI) applications. APPROACH: A novel method based on covariance matrix descriptors, which lie in Riemannian manifold, and the relevance vector machines classifier is proposed. The method is applied on electroencephalographic (EEG) signals and tested in multiple subjects. MAIN RESULTS: The method is shown to outperform other approaches in the field with respect to accuracy and robustness. The algorithm is validated on various categories of speech, such as imagined pronunciation of vowels, short words and long words. The classification accuracy of our methodology is in all cases significantly above chance level, reaching a maximum of 70% for cases where we classify three words and 95% for cases of two words. SIGNIFICANCE: The results reveal certain aspects that may affect the success of speech imagery classification from EEG signals, such as sound, meaning and word complexity. This can potentially extend the capability of utilizing speech imagery in future BCI applications. The dataset of speech imagery collected from total 15 subjects is also published.

Spontaneous postpartum rupture of an intact uterus: a case report.

Rupture of uterus is an obstetrical complication characterized by a breach in the uterine wall and the overlying serosa. We report an unusual case of spontaneous rupture of an unscarred uterus in a 33-year-old woman, a day after her third successful vaginal delivery. A 33-year-old pregnant woman, gravid 3, para 3, was referred to our department at 39 gestational week because of rupture of membranes. Despite tocolysis administration, her pregnancy was delivered vaginally after 2 days, giving birth to a male neonate of 3,020 g with normal Apgar scores at first and fifth minute. Her uterus was intact and gynecological examination after delivery was normal without any potential signs or symptoms of pathology. However, the day following her labor, patient complained of left iliac fossa pain. Her blood tests revealed a CRP value at 27.6 mg/L, whereas the X-rays revealed an extensive impacted fecal mass in the colon. MRI revealed that the left lower myometrial part of the uterus was depicted abrupt, with simultaneous presence of hemorrhagic stuff. The decision of laparotomy was therefore made in order to further evaluate rupture of uterus and properly treat patient. And subtotal hysterectomy was performed. Postoperative follow-up period was not characterized by any complications and patient was finally discharged 4 days after hysterectomy.

Conservative management of cervical pregnancy with intramuscular administration of methotrexate and KCl injection: Case report and review of the literature.

We report the case of a cervical pregnancy successfully treated with intramuscular injection of methotrexate (MTX) and intramniotic administration of potassium chloride. A 41-year-old woman was admitted to our Department with the suspicion of ectopic pregnancy. Transvaginal ultrasound revealed empty endometrial cavity, gestational sac within the cervical canal and embryonic echo measuring crown rump length 1.5 mm. Serum beta human chorionic gonadotropine (ß-HCG) was measured 28590 IU/L. No cardiac activity was detected. The diagnosis of a cervical pregnancy was made. Patient was treated with intramuscular administration of methotrexate (50 mg/m(2)) in combination with ultrasound-guided intramniotic injection of KCl (2 meq/mL). Gradual decrease of ß-HCG levels as well as ultrasound observation of collapsed gestational sac was observed. No curettage was necessitated. Patient was discharged on day 10(th) and was set in follow-up on a weekly basis. ß-HCG values were measured < 10 IU/L on 56(th) day after MTX administration. Intramuscular administration of MTX may be effective in treatment of cervical pregnancy without additional interventional measures.