No abstract

Electroencephalogram (EEG) signal classification plays an important role to facilitate physically impaired patients by providing brain-computer interface (BCI)-controlled devices. However, practical applications of BCI make it difficult to decode motor imagery-based brain signals for multiclass classification due to their non-stationary nature. In this study, we aim to improve multiclass classification accuracy for motor imagery movement using sub-band common spatial patterns with sequential feature selection (SBCSP-SBFS) method. Filter bank having bandpass filters of different overlapped frequency cutoffs is applied to suppress the noise signals from raw EEG signals. The output of these sub-band filters is sent for feature extraction by applying common spatial pattern (CSP) and linear discriminant analysis (LDA). As all of the extracted features are not necessary for classification therefore, selection of optimal features is done by passing the extracted features to sequential backward floating selection (SBFS) technique. Three different classifiers were then trained on these optimal features, i.e., support vector machine (SVM), Naïve-Bayesian Parzen-Window (NBPW), and k-Nearest Neighbor (KNN). Results are evaluated on two datasets, i.e., Emotiv Epoc and wet gel electrodes for three classes, i.e., right-hand motor imagery, left hand motor imagery, and rest state. The proposed model yields a maximum accuracy of 60.61% in case of Emotiv Epoc headset and 86.50% for wet gel electrodes. The computed accuracy shows an increase of 7% as compared to previously implemented multiclass EEG classification.

No abstract

A low-cost microwave sensor was designed for oil adulteration detection and characterization of pure edible oil using dielectric spectroscopy. The sensor’s final design was fabricated on a low cost 1.6 mm thick FR-4 substrate with a combination of a complementary split ring resonator and a transmission line. The sensor’s dimensions were 35 × 30 × 1.6 mm3 with a substrate dielectric constant of 4.3. A 5.25 GHz resonance frequency was selected as a reference for characterization and adulteration detection in pure edible oil. Initially, pure olive, caster, flaxseed, and mustard oil were characterized by the design sensors, with frequency shifts of 250, 370, 150, and 320 MHz, respectively. Pure olive oil with adulteration of castor, mustard, and argemone oil, was tested by placing the samples directly on the sensor. The experimental results showed that the sensor can detect 10% to 30% adulteration in the olive oil. The maximum sensitivity, frequency shift and quality factor were noted as 4.6, 530 MHz and 39, respectively. The high values of sensitivity and quality factor, along with agreement between simulated and experimental results, makes our sensor a good candidate for oil characterization and adulteration detection.

No abstract