Detection of intention level in response to task difficulty from EEG signals

We present an approach that enables detecting intention levels of subjects in response to task difficulty utilizing an electroencephalogram (EEG) based brain-computer interface (BCI). In particular, we use linear discriminant analysis (LDA) to classify event-related synchronization (ERS) and desynchronization (ERD) patterns associated with right elbow flexion and extension movements, while lifting different weights. We observe that it is possible to classify tasks of varying difficulty based on EEG signals. Additionally, we also present a correlation analysis between intention levels detected from EEG and surface electromyogram (sEMG) signals. Our experimental results suggest that it is possible to extract the intention level information from EEG signals in response to task difficulty and indicate some level of correlation between EEG and EMG. With a view towards detecting patients' intention levels during rehabilitation therapies, the proposed approach has the potential to ensure active involvement of patients throughout exercise routines and increase the efficacy of robot assisted therapies

Brain computer interface based robotic rehabilitation with online modification of task speed

We present a systematic approach that enables online modification/adaptation of robot assisted rehabilitation exercises by continuously monitoring intention levels of patients utilizing an electroencephalogram (EEG) based Brain-Computer Interface (BCI). In particular, we use Linear Discriminant Analysis (LDA) to classify event-related synchronization (ERS) and desynchronization (ERD) patterns associated with motor imagery; however, instead of providing a binary classification output, we utilize posterior probabilities extracted from LDA classifier as the continuous-valued outputs to control a rehabilitation robot. Passive velocity field control (PVFC) is used as the underlying robot controller to map instantaneous levels of motor imagery during the movement to the speed of contour following tasks. In other words, PVFC changes the speed of contour following tasks with respect to intention levels of motor imagery. PVFC also allows decoupling of the task and the speed of the task from each other, and ensures coupled stability of the overall robot patient system. The proposed framework is implemented on AssistOn-Mobile - a series elastic actuator based on a holonomic mobile platform, and feasibility studies with healthy volunteers have been conducted test effectiveness of the proposed approach. Giving patients online control over the speed of the task, the proposed approach ensures active involvement of patients throughout exercise routines and has the potential to increase the efficacy of robot assisted therapies

Control of a BCI-based upper limb rehabilitation system utilizing posterior probabilities (BBA tabanlı üst uzuv rehabilitasyon sisteminin sonsal olasılık değerleri kullanılarak kontrolü)

In this paper, an electroencephalogram (EEG) based Brain-Computer Interface (BCI) is integrated with a robotic system designed to target rehabilitation therapies of stroke patients such that patients can control the rehabilitation robot by imagining movements of their right arm. In particular, the power density of frequency bands are used as features from the EEG signals recorded during the experiments and they are classified by Linear Discriminant Analysis (LDA). As one of the novel contributions of this study, the posterior probabilities extracted from the classifier are directly used as the continuous-valued outputs, instead of the discrete classification output commonly used by BCI systems, to control the speed of the therapeutic movements performed by the robotic system. Adjusting the exercise speed of patients online, as proposed in this study, according to the instantaneous levels of motor imagery during the movement, has the potential to increase efficacy of robot assisted therapies by ensuring active involvement of patients. The proposed BCI-based robotic rehabilitation system has been successfully implemented on physical setups in our laboratory and sample experimental data are presented

Association Between Body Mass Index and Meniscal Tears Requiring Surgery

Aim: This study evaluated the association between body mass index (BMI) and operated patients due to meniscal injuries of the knee. We also investigated the influence of BMI on meniscal tears with regard to age. Methods: We investigated 104 patients who had surgery for meniscal injuries and 111 patients who had knee magnetic resonance imaging (MRI) with no prior history of meniscal surgery. The relationship between BMI and meniscal injuries which required meniscal surgery was evaluated by independent samples T-test. A cutoff BMI value has been tried to find out by receiver operating characteristics (ROC). The odds ratio has been calculated with regard to this cutoff value. Patients were classified into three age groups ≤30, 31–50 and ≥51 years old. Chi-square was used to determine whether age affected the BMI relationship with meniscal injuries which required surgery. Results: BMI values were significantly higher in surgical patients compared to controls (p = 0.005). To compare surgical and non-surgical patients, ROC analysis was used and area under curve (AUC) value was calculated as 0.605. A BMI value of 27.90 had the highest specificity (92.0%) and sensitivity (40.4%), and the odds ratio calculated by Pearson chi-square was 3,08 for this BMI value. The most significant difference in BMI between surgical and non-surgical patients was observed in the 31–50 age group (p = 0.007). There was no significant difference in BMI between surgical and non-surgical patients in the <30 age group (p=0,404). Conclusion: Higher BMI increases the risk of meniscal tears requiring surgery, especially in the 31–50 age groups. Patients might benefit from weight regulation since BMI is thought to be an important modifiable risk factor for meniscal tears

Classification of motor task execution speed from EEG data

It is believed that the obtention of instantaneous intention level from electroencephalogram (EEG) signals and its use as a control signal may increase the benefits gained from the robotic rehabilitation process of stroke patients. This paper investigates a method for classifying the speed of arm movements from EEG recordings of healthy subjects under the assumption that the intention level of a patient may be reflected in motor task execution velocity. Experimental data were collected from eight (four male, four female) healthy volunteers while they were performing right arm movements at two different speeds. We designed an experiment in which the subjects were asked to carry a glass cup in two different environments: nail or cotton. The task speeds for both environments were decided individually by the volunteers; however the nail environment had a maximum speed limit. Participants were warned by a crashing glass audio stimulus if they exceeded the speed limit of the nail environment. As a result, a simple daily life activity was performed at two different speeds as an experimental task. Based on experimental data from eight healthy subjects, we successfully classified two different speed levels and resting state from event related synchronization (ERS) and event related desynchronization (ERD) patterns of EEG signals by linear discriminant analysis (LDA) classifier. Results reveal that LDA can discriminate different velocity levels when six frequency bands of three EEG recording channels were used as the feature vector

Detection of motor task difficulty level from EEG data (EEG verisinden motor hareketi zorluk seviyesinin tespiti)

Rehabilitation protocols are used to increase daily life activities of locked-in patients. There are ongoing efforts to use brain-computer interfaces (BCI) in various ways to increase the benefits of such rehabilitation protocols to patients. An interesting claim is that if a system can detect the intention level of a patient and update the daily program according to this patient's motivation, the gain from these rehabilitation protocol can be increased. In this study, a system that records the electroencephalography (EEG) signals of healthy users performing arm movements against two levels of force has been designed based on the assumption that intention level is proportional to the level of motor task difficulty. EEG signals from 7 healthy subjects and 3 channels were recorded while subjects were performing work against two different levels of force. We calculated frequency bands of these channels and applied linear discriminant analysis (LDA) for classification of two environments corresponding to two motor task difficulty levels and resting state

Evaluation of oxidative stress in degenerative rotator cuff tears

Background: Oxidative stress occurs as a result of the disruption of the balance between the formations of reactive oxygen species and antioxidant defense mechanisms during the conversion of nutrients into energy. Increased body oxidative stress has been reported to be involved in the etiology of several degenerative and chronic diseases. We hypothesized that the body oxidative stress level is higher in patients with atraumatic degenerative rotator cuff tear than that in healthy individuals. Methods: The patients who underwent arthroscopic repair for atraumatic, degenerative rotator cuff tear were prospectively evaluated. A total of 30 patients (group 1, 19 females and 11 males; mean age: 57.33 ± 6.96 years; range: 50-77 years) and 30 healthy individuals (group 2, 18 females and 12 males; mean age: 56.77 ± 6 years; range: 51-72 years) were included in the study. The Constant and American Shoulder and Elbow Surgeons scoring systems were used to evaluate the clinical outcomes. Serum oxidative stress parameters of the patients and the control group were biochemically evaluated. Accordingly, thiol/disulfide (DS) balance (DS/native thiol [NT], DS/total thiol [TT]), Total Oxidant Status (TOS), oxidative stress index, and nuclear factor erythroid-2–associated factor-2 values were used as the biochemical parameters indicating an increase in the serum oxidative stress level. Total antioxidant status and NT/TT values served as the biochemical parameters indicating a decrease in the serum oxidative stress level. Results: The study follow-up duration was 12 months. A statistically significant increase was observed in American Shoulder and Elbow Surgeons and Constant scores of patients who underwent arthroscopic rotator cuff repair relative to that during the preoperative period (P = .01). The values of biochemical parameters (DS/NT, DS/TT, TOS, oxidative stress index, and nuclear factor erythroid-2–associated factor-2), which indicated an increase in the serum oxidative stress, were significantly higher in preoperative patients than those in postoperative patients, albeit the control group values were significantly lower than those of the postoperative patients. The biochemical parameters (NT/TT and total antioxidant status) indicating a decrease in the serum oxidative stress levels were significantly higher in the postoperative patients than those in the preoperative patients and significantly lower than those in the control group. Conclusion: High levels of markers indicating an increase in the serum oxidative stress in patients with degenerative rotator cuff rupture suggested that TOS may be involved in the etiopathogenesis of rotator cuff degeneration. Although the oxidative load decreases during the postoperative period, the fact that it is still higher than that in healthy individuals supports this claim. © 2022 Journal of Shoulder and Elbow Surgery Board of Trustee

A scrutiny study on wave energy potential and policy in Turkey

Recently new and renewable energy sources began to become prominent as alternatives to fossil fuels. Among these are wind, solar, hydraulic, biomass, geothermal and wave energies. As for Turkey, the least accounted and less applied of these sources is wave energy. The government has established a short-term outlook on utilization of renewable energy sources, named “National Renewable Energy Action Plan” which is a part of Vision 2023 targets. Nonetheless, there is no planned utilization of and/or investment into wave energy in Turkey’s agenda up to the year 2023. This might be mainly because of the complex structure of wave energy conversion systems, marine conditions, mechanical difficulties and high initial investment costs. However, this type of energy is environmentally friendly, cheap and clean, and a great potential is available especially in Turkey which is surrounded on three sides by sea. Although Turkey has neither coasts to oceans nor a long stretch of west coastline, which have the highest energetic waves thanks to the prevailing west-to-east winds; the Black Sea basin, as well as the south-western Mediterranean region, may offer a good potential for development as an energetic regime, often comparable to oceanic sites in terms of wave heights, induced by strong wind patterns. In this study, wave energy potential in Turkey and recent studies made on determination of suitable sites for evaluation of wave energy in Turkey are discussed

Diesel engine NOx emission modeling using a new experiment design and reduced set of regressors

n this paper, NOx emissions from a diesel engine are modeled with nonlinear autoregressive with exogenous input (NARX) model. Airpath and fuelpath channels are excited by chirp signals where the frequency profile of each channel is generated by increasing the number of sweeps. Past values of the output are employed only in linear prediction with all input regressors, and the most significant input regressors are selected for the nonlinear prediction by orthogonal least square (OLS) algorithm and error reduction ratio. Experimental results show that NOx emissions can be modeled with high validation performance and models obtained using a reduced set of regressors perform better in terms of stability and robustness