A novel framework for classification of two-class motor imagery EEG signals using logistic regression classification algorithm.

Robotics and artificial intelligence have played a significant role in developing assistive technologies for people with motor disabilities. Brain-Computer Interface (BCI) is a communication system that allows humans to communicate with their environment by detecting and quantifying control signals produced from different modalities and translating them into voluntary commands for actuating an external device. For that purpose, classification the brain signals with a very high accuracy and minimization of the errors is of profound importance to the researchers. So in this study, a novel framework has been proposed to classify the binary-class electroencephalogram (EEG) data. The proposed framework is tested on BCI Competition IV dataset 1 and BCI Competition III dataset 4a. Artifact removal from EEG data is done through preprocessing, followed by feature extraction for recognizing discriminative information in the recorded brain signals. Signal preprocessing involves the application of independent component analysis (ICA) on raw EEG data, accompanied by the employment of common spatial pattern (CSP) and log-variance for extracting useful features. Six different classification algorithms, namely support vector machine, linear discriminant analysis, k-nearest neighbor, naïve Bayes, decision trees, and logistic regression, have been compared to classify the EEG data accurately. The proposed framework achieved the best classification accuracies with logistic regression classifier for both datasets. Average classification accuracy of 90.42% has been attained on BCI Competition IV dataset 1 for seven different subjects, while for BCI Competition III dataset 4a, an average accuracy of 95.42% has been attained on five subjects. This indicates that the model can be used in real time BCI systems and provide extra-ordinary results for 2-class Motor Imagery (MI) signals classification applications and with some modifications this framework can also be made compatible for multi-class classification in the future.

Artificial Immune System-Negative Selection Classification Algorithm (NSCA) for Four Class Electroencephalogram (EEG) Signals.

Artificial immune systems (AIS) are intelligent algorithms derived from the principles inspired by the human immune system. In this study, electroencephalography (EEG) signals for four distinct motor movements of human limbs are detected and classified using a negative selection classification algorithm (NSCA). For this study, a widely studied open source EEG signal database (BCI IV-Graz dataset 2a, comprising nine subjects) has been used. Mel frequency cepstral coefficients (MFCCs) are extracted as selected features from recorded EEG signals. Dimensionality reduction of data is carried out by applying two hidden layered stacked auto-encoder. Genetic algorithm (GA) optimized detectors (artificial lymphocytes) are trained using negative selection algorithm (NSA) for detection and classification of four motor movements. The trained detectors consist of four sets of detectors, each set is trained for detection and classification of one of the four movements from the other three movements. The optimized radius of detector is small enough not to mis-detect the sample. Euclidean distance of each detector with every training dataset sample is taken and compared with the optimized radius of the detector as a nonself detector. Our proposed approach achieved a mean classification accuracy of 86.39% for limb movements over nine subjects with a maximum individual subject classification accuracy of 97.5% for subject number eight.

Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis.

Brain Computer Interface (BCI) determines the intent of the user from a variety of electrophysiological signals. These signals, Slow Cortical Potentials, are recorded from scalp, and cortical neuronal activity is recorded by implanted electrodes. This paper is focused on design of an embedded system that is used to control the finger movements of an upper limb prosthesis using Electroencephalogram (EEG) signals. This is a follow-up of our previous research which explored the best method to classify three movements of fingers (thumb movement, index finger movement, and first movement). Two-stage logistic regression classifier exhibited the highest classification accuracy while Power Spectral Density (PSD) was used as a feature of the filtered signal. The EEG signal data set was recorded using a 14-channel electrode headset (a noninvasive BCI system) from right-handed, neurologically intact volunteers. Mu (commonly known as alpha waves) and Beta Rhythms (8-30 Hz) containing most of the movement data were retained through filtering using "Arduino Uno" microcontroller followed by 2-stage logistic regression to obtain a mean classification accuracy of 70%.

Efficient FIR Filter Implementations for Multichannel BCIs Using Xilinx System Generator.

Background. Brain computer interface (BCI) is a combination of software and hardware communication protocols that allow brain to control external devices. Main purpose of BCI controlled external devices is to provide communication medium for disabled persons. Now these devices are considered as a new way to rehabilitate patients with impunities. There are certain potentials present in electroencephalogram (EEG) that correspond to specific event. Main issue is to detect such event related potentials online in such a low signal to noise ratio (SNR). In this paper we propose a method that will facilitate the concept of online processing by providing an efficient filtering implementation in a hardware friendly environment by switching to finite impulse response (FIR). Main focus of this research is to minimize latency and computational delay of preprocessing related to any BCI application. Four different finite impulse response (FIR) implementations along with large Laplacian filter are implemented in Xilinx System Generator. Efficiency of 25% is achieved in terms of reduced number of coefficients and multiplications which in turn reduce computational delays accordingly.

Prostacyclin and milrinone by aerosolization improve pulmonary hemodynamics in newborn lambs with experimental pulmonary hypertension.

Aerosolized prostacyclin (PGI2) produces selective pulmonary vasodilation in patients with pulmonary hypertension (PH). The response to PGI2 may be increased by phosphodiesterase type 3 inhibitors such as milrinone. We studied the dose response effects of aerosolized PGI2 and aerosolized milrinone both alone and in combination on pulmonary and systemic hemodynamics in newborn lambs with Nomega-nitro-L-arginine methyl ester (L-NAME)-induced PH. We hypothesized that coaerosolization of PGI2 with milrinone would additively decrease pulmonary vascular resistance (PVR), prolong the duration of action of PGI2, and selectively dilate the pulmonary vasculature. Near-term lambs were delivered by C-section and instrumented and PH was induced by L-NAME (bolus 25 mg/kg; infusion 10 mg.kg(-1).h(-1)) and indomethacin. In the first set of experiments, PGI2 was aerosolized at random doses of 2, 20, 100, 200, 500, and 1,000 ng.kg(-1).min(-1) followed by milrinone at doses of 0.1, 1, and 10 microg.kg(-1).min(-1) over 10 min. In the second set of experiments, milrinone at 1 microg.kg(-1).min(-1) was aerosolized in combination with PGI2 at doses of 20, 100, and 200 ng.kg(-1).min(-1) over 10 min. Pulmonary arterial pressures (PAP) and PVR decreased significantly with increasing doses of aerosolized PGI2 and milrinone. The combination of PGI2 and milrinone significantly reduced PAP and PVR more than either of the drugs aerosolized alone. Addition of milrinone significantly increased the duration of action of PGI2. When aerosolized independently, PGI2 and milrinone selectively dilated the pulmonary vasculature but the combination did not. Milrinone enhances the vasodilatory effects of PGI2 on the pulmonary vasculature but caution must be exercised regarding systemic hypotension.

Effects of prostacyclin and milrinone on pulmonary hemodynamics in newborn lambs with persistent pulmonary hypertension induced by ductal ligation.

Prostacyclin (PGI(2)) stimulates adenyl cyclase to synthesize cAMP within the vascular smooth muscle resulting in vasodilatation. Milrinone inhibits cAMP clearance by phosphodiesterase type III. We studied the dose response of pulmonary and systemic hemodynamics to intratracheal (IT) PGI(2) in newborn lambs with pulmonary hypertension (PH) and whether intravenous milrinone potentiate these effects. IT-PGI(2) at varying doses was administered to lambs with PH induced by prenatal ductal ligation. IT-PGI(2) doses were repeated in the presence of intravenous milrinone (bolus-100 microg/kg followed by infusion at 1 microg/kg/min). Increasing doses of IT-PGI(2) significantly decreased mean pulmonary arterial pressures (PAP) and pulmonary vascular resistance (PVR) and increased pulmonary blood flow (PBF). Intravenous milrinone by itself produced a significant reduction in PVR and a significant increase in PBF. Intravenous milrinone significantly shortened the onset, prolonged the duration and degree of pulmonary vasodilation produced by PGI(2). We conclude that intravenous milrinone potentiates the pulmonary vasodilator effects of PGI(2) at lower doses.