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Naseer, Noman,Qureshi, Nauman Khalid,Noori, Farzan Majeed,Hong, Keum-Shik Hindawi Publishing Corporation 2016 Computational intelligence and neuroscience Vol.2016 No.-
<P>We analyse and compare the classification accuracies of six different classifiers for a two-class mental task (mental arithmetic and rest) using functional near-infrared spectroscopy (fNIRS) signals. The signals of the mental arithmetic and rest tasks from the prefrontal cortex region of the brain for seven healthy subjects were acquired using a multichannel continuous-wave imaging system. After removal of the physiological noises, six features were extracted from the oxygenated hemoglobin (HbO) signals. Two- and three-dimensional combinations of those features were used for classification of mental tasks. In the classification, six different modalities, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), <I>k</I>-nearest neighbour (<I>k</I>NN), the Naïve Bayes approach, support vector machine (SVM), and artificial neural networks (ANN), were utilized. With these classifiers, the average classification accuracies among the seven subjects for the 2- and 3-dimensional combinations of features were 71.6, 90.0, 69.7, 89.8, 89.5, and 91.4% and 79.6, 95.2, 64.5, 94.8, 95.2, and 96.3%, respectively. ANN showed the maximum classification accuracies: 91.4 and 96.3%. In order to validate the results, a statistical significance test was performed, which confirmed that the<I> p </I>values were statistically significant relative to all of the other classifiers (<I>p</I> < 0.005) using HbO signals. </P>
Noman Naseer,Keum-Shik Hong,M. Jawad Khan,M. Raheel Bhutta 제어로봇시스템학회 2015 제어로봇시스템학회 국제학술대회 논문집 Vol.2015 No.10
In this paper we analyze and compare the performance of support vector machine (SVM) and artificial neural network (ANN) for classification of fNIRS signals. fNIRS signals due to mental arithmetic and mental counting are acquired from the prefrontal cortex of ten healthy subjects. After preprocessing and filtering, SVM and ANN classification is performed on the same feature set ? mean and slope of the changes in concentration of oxy-hemoglobin. Although no significant difference in the average classification accuracies, obtained using SVM and ANN, is observed (p = 0.2); it is noted that the standard deviation of classification accuracies using ANN is significantly higher than that of SVM. Furthermore, the computational speed of SVM is significantly higher than that of ANN. It is concluded that SVM offers stable classification accuracies and fast computation as compared to ANN.
Drowsiness Detection in Dorsolateral-Prefrontal Cortex using fNIRS for a Passive-BCI
M. Jawad Khan,Keum-Shik Hong,Noman Naseer,M. Raheel Bhutta 제어로봇시스템학회 2015 제어로봇시스템학회 국제학술대회 논문집 Vol.2015 No.10
In this paper, we have investigated the feasibility of detecting drowsiness using hemodynamic brain signals for a passive brain-computer interface (BCI). Functional near-infrared spectroscopy (fNIRS) is used to measure the right dorsolateral-prefrontal brain region in order to investigate the hemodynamic changes corresponding to drowsy and alert states. The data is recorded using five drowsy subjects during a simulated car driving task. The recoded data are converted into oxy- and deoxy-hemoglobin (HBO and HbR) using the modified Beer-Lambert law (MBLL) for feature extraction and classification. Signal mean and signal slope are extracted using the spatio-temporal time windows as features. Linear discriminant analysis (LDA) and support vector machines (SVM) are used for the training and testing of the brain data. The classification accuracy obtained using offline analyses is 74% and 77% respectively. The results show that drowsy and alert states are distinguishable from the right dorsolateral prefrontal brain region. Also, fNIRS modality can be used for drowsiness detection for a passive BCI.
A hybrid EEG-fNIRS BCI: motor imagery for EEG and mental arithmetic for fNIRS
M. Jawad Khan,Keum-Shik Hong,Noman Naseer,M. Raheel Bhutta 제어로봇시스템학회 2014 제어로봇시스템학회 국제학술대회 논문집 Vol.2014 No.10
In this paper, we have combined electroencephalography (EEG) and functional near-infrared spectroscopy (fNRIS) to make a hybrid EEG-NIRS based system for brain-computer interface (BCI). The EEG electrodes were placed on the motor cortex region and the NIRS optodes were set on the prefrontal region. The data of four subjects was acquired using mental arithmetic tasks and motor imageries of the left- and right-hand. The EEG data were band-pass filtered to obtain the activity (8~18 Hz). The modified Beer-Lambert law (MBLL) was used to convert the fNIRS data into oxy- and deoxy-hemoglobin (HbO and HbR), respectively. A common threshold between the two modalities was established to define a common resting state. The support vector machines (SVM) was used for data classification. Three control commands were generated using the prefrontal and motor cortex data. The results show that EEG and fNIRS can be combined for better brain signal acquisition and classification for BCI.
Xiaoyue Sang,Zhaohui Yuan,Xiaojun Yu,Muhammad Tariq Sadiq,Na Liang,Noman Naseer,GaoXi Xiao 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.7
The surface temperature of workpieces in a multi-temperature zone sintering furnace is an important parameter to characterize the performances of a multi-temperature zone system. Due to the practical structural properties of the sintering furnace, however, the conventional way of temperature measurement cannot detect the exact surface temperature of the workpieces directly, making it difficult to control the multi-temperature zone system performances precisely. To address such an issue, this paper proposes, for the first time to the best of our knowledge, a temperature compensation algorithm based soft-measurement technique to compensate for the temperature of the measuring points. Specifically, we analyze the heat-transfer mechanism within the electric heating surface and the workpiece surface and establish a mathematical model for it first, and then calculate the radiative heat transfer coefficient between the diffuse gray surface within sintering furnace using the discrete radiation heat transfer method. Finally, the heat transfer mechanism based soft measurement technique is proposed and applied tocompensate for the temperature. Both simulations and experiments with a practical sintering furnace are conducted to verify the correctness and effectiveness of the proposed temperature compensation algorithm in different cases. Results show that the proposed algorithm could help maintain the temperature difference within a range limit of ±5◦, which is much better as compared with the conventional temperature measurement methods.