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Hybrid EEG–fNIRS-Based Eight-Command Decoding for BCI: Application to Quadcopter Control
Khan, Muhammad Jawad,Hong, Keum-Shik Frontiers Media S.A. 2017 Frontiers in neurorobotics Vol.11 No.-
<P>In this paper, a hybrid electroencephalography–functional near-infrared spectroscopy (EEG–fNIRS) scheme to decode eight active brain commands from the frontal brain region for brain–computer interface is presented. A total of eight commands are decoded by fNIRS, as positioned on the prefrontal cortex, and by EEG, around the frontal, parietal, and visual cortices. Mental arithmetic, mental counting, mental rotation, and word formation tasks are decoded with fNIRS, in which the selected features for classification and command generation are the peak, minimum, and mean ΔHbO values within a 2-s moving window. In the case of EEG, two eyeblinks, three eyeblinks, and eye movement in the up/down and left/right directions are used for four-command generation. The features in this case are the number of peaks and the mean of the EEG signal during 1 s window. We tested the generated commands on a quadcopter in an open space. An average accuracy of 75.6% was achieved with fNIRS for four-command decoding and 86% with EEG for another four-command decoding. The testing results show the possibility of controlling a quadcopter online and in real-time using eight commands from the prefrontal and frontal cortices <I>via</I> the proposed hybrid EEG–fNIRS interface.</P>
An improved co-training approach for document Sentiment classification
Jawad Khan(자와드 칸),Aftab Alam(아프타 발람),Muhammad Numan Khan(무함마드 누만 칸),Irfan Ullah(이르판 울라),Muhammad Umair(무하마드 우매르),Umair Qudus(구두스 우매르),Tariq Habib Afridi(타리크 하비브 아프리디),Sung Soo Park(박성수),Young-Koo Lee( 한국정보과학회 2020 한국정보과학회 학술발표논문집 Vol.2020 No.7
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.
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.
Burden of Virus-associated Liver Cancer in the Arab World, 1990-2010
Khan, Gulfaraz,Hashim, M. Jawad Asian Pacific Journal of Cancer Prevention 2015 Asian Pacific journal of cancer prevention Vol.16 No.1
Hepatocellular carcinoma (HCC) is amongst the top three cancer causes of death worldwide with hepatitis B and C viruses (HBV/HCV) as the main etiological agents. An up-to-date descriptive epidemiology of the burden of HBV/HCV-associated HCC in the Arab world is lacking. We therefore determined the burden of HBV/HCV-associated HCC deaths in the Arab world using the Global Burden of Disease (GBD) 2010 dataset. GBD 2010 provides, for the first time, deaths specifically attributable to viral-associated HCC. We analyzed the data for the 22 Arab countries by age, sex and economic status from 1990 to 2010 and compared the findings to global trends. Our analysis revealed that in 2010, an estimated 752,101 deaths occurred from HCC worldwide. Of these 537,093 (71%) were from HBV/HCV-associated HCC. In the Arab world, 17,638 deaths occurred from HCC of which 13,558 (77%) were HBV/HCV-linked. From 1990 to 2010, the burden of HBV and HCV-associated HCC deaths in the Arab world increased by 137% and 216% respectively, compared to global increases of 62% and 73%. Age-standardized death rates also increased in most of the Arab countries, with the highest rates noted in Mauritania and Egypt. Male gender and low economic status correlated with higher rates. These findings indicate that the burden of HBV/HCV-associated HCC in the Arab world is rising at a much faster rate than rest of the world and urgent public health measures are necessary to abate this trend and diminish the impact on already stretched regional healthcare systems.