Real-time feature extraction of P300 component using adaptive nonlinear principal component analysis

Turnip, Arjon; Hong, Keum–Shik; Jeong, Myung-Yung

doi:10.1186/1475-925x-10-83

Cited by 103 publications

(50 citation statements)

References 42 publications

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“…Non-invasive brain-computer interface (BCI) methods measure brain activities either by detecting the electrophysiological signals [1][2][3] or by determining the hemodynamic responses [4][5][6][7][8][9][10]. The electrophysiological phenomena are generated due to neuronal firing as a result of brain tasks [1].…”

Section: Introductionmentioning

confidence: 99%

Passive BCI based on drowsiness detection: an fNIRS study

Khan

Hong

2015

Biomed. Opt. Express

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Abstract:We use functional near-infrared spectroscopy (fNIRS) to discriminate the alert and drowsy states for a passive brain-computer interface (BCI). The passive brain signals for the drowsy state are acquired from the prefrontal and dorsolateral prefrontal cortex. The experiment is performed on 13 healthy subjects using a driving simulator, and their brain activity is recorded using a continuous-wave fNIRS system. Linear discriminant analysis (LDA) is employed for training and testing, using the data from the prefrontal, left-and right-dorsolateral prefrontal regions. For classification, eight features are tested: mean oxyhemoglobin, mean deoxyhemoglobin, skewness, kurtosis, signal slope, number of peaks, sum of peaks, and signal peak, in 0~5, 0~10, and 0~15 second time windows, respectively. The results show that the best performance for classification is achieved using mean oxyhemoglobin, the signal peak, and the sum of peaks as features. The average accuracies in the right dorsolateral prefrontal cortex (83.1, 83.4 and 84.9% in the 0~5, 0~10 and 0~15 second time windows, respectively) show that the proposed method has an effective utility for detection of drowsiness for a passive BCI.

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Section: Introductionmentioning

confidence: 99%

Passive BCI based on drowsiness detection: an fNIRS study

Khan

Hong

2015

Biomed. Opt. Express

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185

136

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“…However, their method requires a certain number of electrodes to run ICA in order to guarantee the efficiency of the SDA. The adaptive nonlinear principal component analysis (ANPCA) [43] and ICA-based method [44] are also blind source separation-based spatial filtering methods to enhance the SNR of P300, and therefore a large number of EEG channels is required (12 for the former and eight for the latter). However, these promising spatial filtering techniques could not be directly applied to our current system because only two electrodes at Pz and Cz are used for P300 detection in our current BCI system.…”

Section: Comparison With Other Workmentioning

confidence: 99%

A Self-Paced P300 Healthcare Brain-Computer Interface System with SSVEP-Based Switching Control and Kernel FDA + SVM-Based Detector

Liu

Wang

2016

Applied Sciences

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This paper presents a novel brain-computer interface (BCI)-based healthcare control system, which is based on steady-state visually evoked potential (SSVEP) and P300 of electroencephalography (EEG) signals. The proposed system is composed of two modes, a brain switching mode and a healthcare function selection mode. The switching mode can detect whether a user has the intent to activate the function selection mode by detecting SSVEP in an ongoing EEG. During the function selection mode, the user is able to select any functions that he/she wants to activate through a healthcare control panel, and the function selection is done by detecting P300 in the user's EEG signals. The panel provides 25 functions representing 25 frequently performed activities of daily life. Therefore, users with severe motor disabilities can activate the system and any functions in a self-paced manner, achieving the goal of autonomous healthcare. To achieve high P300 detection accuracy, a novel P300 detector based on kernel Fisher's discriminant analysis (kernel FDA) and support vector machine (SVM) is also proposed. Experimental results, carried out on five subjects, show that the proposed BCI system achieves high SSVEP detection (93%) and high P300 detection (95.5%) accuracies, meaning that the switching mode has a high sensitivity, and the function selection mode has the ability to accurately detect the functions that the users want to trigger. More important, only three electrodes (Oz, Cz, and Pz) are required to measure EEG signals, enabling the system to have good usability in practical use.

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“…Electroencephalogram (EEG) power analysis are one of veasible tool for examining the effects of drugs on brain function. Due to its wide availability, relatively low-cost, superior temporal resolution, easy implementation, and non-invasiveness, intensive research has been performed on EEG as tool for cognitive processes in research and clinical diagnose [11][12][13][14][15][16][17][18]. The EEG pattern is constantly changing depending on mental activity, relaxation, drowsiness and sleep and this dynamic process is therefore an index of cortical activation, cognitive function and consciousness.…”

Section: Introductionmentioning

confidence: 99%

Detection of Drug Effects on Brain Activity using EEG-P300 with Similar Stimuli

Turnip

Esti

Amri

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. Drug addiction poses a serious problem to our species. The worry that our significant family might be involved in drug use and are concerned to know how to detect drug use. Examinations of thirty taped EEG recordings were performed. The subjects consist of three group: addictive, methadone treatment (rehabilitation), and control (normal) which 10 subjects for each group. Statistical analysis was performed for the relative frequency of wave bands. The higher average amplitude is obtained from the addiction subjects. In the comparison with the signals source, channels P3 provide slightly higher average amplitude than other channels for all of subjects. IntroductionDrug addiction poses a serious problem to our species. Consider the disastrous effects caused by the abuse of one of our oldest drugs, alcohol: automobile accidents, fetal alcohol syndrome, cirrhosis of the liver, Korsakoff's syndrome, increased rate of heart disease, and increased rate of intracerebral haemorrhage [1]. Moreover, drug addiction has been associated with a broad range of cognitive deficits in domains, including emotional regulation and motivation, attention and flexibility, working memory, learning, and decision making [2]. For example, cocaine addiction can cause psychotic behavior, brain damage, and death from overdose; and competition for lucrative and illegal markets terrorizes neighborhoods, subverts political and judicial systems, and causes many violent deaths. The drugs stimulate brain mechanisms responsible for positive reinforcement. In addition, most of them also reduce or eliminate unpleasant feelings, some of which are produced by the drugs themselves. What actually seems to happen is that the occurrence of an appetitive stimulus activates a reinforcement mechanism in the brain that increases the likelihood of the most recent response in the present situation. All natural reinforcers that have been studied so far (such as food for a hungry animal, water for a thirsty one, or sexual contact) have one physiological effect in common: They cause the release of dopamine in the nucleus accumbens [3].Perhaps we worry that our significant family might be involved in drug use and are concerned to know how to detect drug use. Regardless of whom the person is and what his or her relationship is to us, it is important to understand how to detect drug use so we can get help for our child, loved one, or business associate. A drug test is a technical analysis of a biological specimen, for example urine, hair, blood, breath, sweat, or oral fluid/saliva to determine the presence or absence of specified parent drugs

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Real-time feature extraction of P300 component using adaptive nonlinear principal component analysis

Cited by 103 publications

References 42 publications

Passive BCI based on drowsiness detection: an fNIRS study

Passive BCI based on drowsiness detection: an fNIRS study

A Self-Paced P300 Healthcare Brain-Computer Interface System with SSVEP-Based Switching Control and Kernel FDA + SVM-Based Detector

Detection of Drug Effects on Brain Activity using EEG-P300 with Similar Stimuli

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