Using electroencephalogram (EEG) signal to control external devices has always been the research focus in the field of brain-computer interface (BCI). This is especially significant for those disabilities who have lost capacity of movements. In this paper, the P300-based BCI and the microcontroller-based wireless radio frequency (RF) technology are utilized to design a smart home control system, which can be used to control household appliances, lighting system, and security devices directly. Experiment results showed that the system was simple, reliable and easy to be populirised.
Brain ; physiology ; Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300 ; Humans

Adult ; Event-Related Potentials, P300 ; physiology ; Female ; Follow-Up Studies ; Humans ; Male ; Schizophrenia ; physiopathology

This is a review of the progress in the study on auditory event-related potentials (ERP) of novel events. Several cognitive problems about the novelty ERP response on brain and the experiment methods are introduced. The mismatch negative potential (MMN) reflects the detection of deviant events, and MMN is related with the novelty P3. Familiarity and attention influence the novelty ERP response. Experiments results showed that the amplitude of ERPs in the frontal area was reduced with the enhancement of familiarity, while that in the parietal area had little change. Attention was helpful in increasing the amplitude of ERPs, and could make the corresponding brain area active. The functional significance of the response of ERP was discussed also.
Auditory Perception ; physiology ; Brain ; physiology ; Electroencephalography ; Environment ; Event-Related Potentials, P300 ; physiology ; Evoked Potentials, Auditory ; physiology ; Humans

The present paper focused on the extraction of event-related potentials on a single sweep under extremely low S/N ratio. Two methods that can efficiently remove spontaneous EEG, ocular artifacts and power line interference were presented based on ARX modeling and independent component analysis (ICA). The former method applied ARX model to the measured compound signal that extensively contained the three kinds of ordinary noises mentioned above, and used ARX algorithm for parametric identification. The latter decomposed the signal by means of independent component analysis. Besides, some of ICA's important decomposing characters and its intrinsic causality were pointed out definitely. According to the practical situation, some modification on FastICA algorithm was also given, so as to implement auto-adaptive mapping of decomposed results to ERP component. Through simulation, both the two ways are proved to be highly capable of signal extraction and S/N ratio improving.
Electroencephalography ; methods ; statistics & numerical data ; Event-Related Potentials, P300 ; physiology ; Evoked Potentials ; physiology ; Humans ; Signal Processing, Computer-Assisted

OBJECTIVETo compare the spatiotemporal pattern of event-related potentials (ERPs) of relational memory retention between color-location binding and semantics-location binding.

METHODSThe ERPs of 19 channels were recorded from 14 healthy subjects while performing the two binding delayed-match-to-sample tasks. A paired-sample t test was used for analysis of both the behavioral performance and ERPs, and statistical parametric mapping (SPM) of the t values was applied to ERPs.

RESULTSBehavioral performances (reaction time and accuracy) in color-location binding were significant better than those of semantics-location binding. The SPM(t) of the ERPs showed significant effects in the middle frontal region and most areas of the parietal lobe (about 200 ms), the bilateral prefrontal and frontal regions (260 approximately 320 ms), and the left occipital region (500 approximately 580 ms).

CONCLUSIONRetention of semantics-location relation involves more brain regions than that of color-location relation, and the largest effect occurs in the frontal region.

Electroencephalography ; Event-Related Potentials, P300 ; physiology ; Frontal Lobe ; physiology ; Humans ; Memory ; physiology ; Retention (Psychology) ; physiology ; Task Performance and Analysis

In the traditional P300 brain-computer interface (BCI) system, the electroencephalogram (EEG) signals can only provide limited information with a low signal-to-noise ratio. A BCI paradigm under visual stimulus was proposed in our study aiming to effectively activate the related brain areas and response signal while dealing with specific cognitive task (mental arithmetic task), so as to enhance the EEG signals. The result was compared with the traditional P300 counting task paradigm. Then the collected EEG data were preprocessed including extracting signal features with coherent averaging method, and analyzing the influences of different experimental paradigms on main components of event related potential (ERP). In the improved paradigm experiments the average increasing rate of P300 amplitude was 6. 83MV (73. 94%). The brain activity from 400ms was more active and lasted longer. Besides, unlike traditional counting task, mental arithmetic task appeared to have apparent activation at 650ms. The results showed that the improved paradigm could activate the related brain areas better and enhance the characteristics of signal. This provides a new system paradigm for BCI.
Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300 ; physiology ; Humans ; Mathematical Concepts ; Mental Processes ; physiology ; Task Performance and Analysis

The event-related potential (ERP) P300 was recorded to analyze the temporal sequences character and the P300 compositions in premeditated and temporary deception. When 15 healthy undergraduates watched the stolen, familiar but not stolen and strange object pictures, the reaction time was recorded and EEG data were collected to analyze the amplitudes of N1, N2 and P3 sub-component of P300 in honesty and deception group respectively through analog theft paradigm. The results showed that the amplitudes of N1, N2 and P3 in premeditated deception group were markedly larger than those in temporary deception, and the reaction time of deception group was longer than that of honesty group. Compared with temporary deception, more attention resources were invested and intensely response conflict was induced by premeditated deception.
Deception ; Electroencephalography ; Event-Related Potentials, P300 ; physiology ; Female ; Humans ; Lie Detection ; Male ; Reaction Time ; physiology ; Young Adult

Event-Related Potentials, P300 ; physiology ; Humans ; Individuality ; Male ; Memory, Short-Term ; physiology ; Neuropsychological Tests ; standards ; Young Adult

Research on the effect of sleep deprivation (SD) is an approach to shedding light on the working mechanism of sleep for cognitive brain function. To explore how SD affects cognitive function, a number of measures involving psychology, brain imaging and electrophysiology, have been adopted in this area. Research groups from domestic and verseas have focused on many aspects of cognitive science and have acquired initial results, but uniform conclusion has not been made because of mixed factors. Starting from the mechanism of SD, this paper summarizes the progress in the search of effect of SD, including the neurophysiologic change by brain imaging and the use of EEG for evaluating the decline of cognitive function, and then analyzes the main influential factors and orientation of future research in this area.
Brain ; physiopathology ; Cognition ; physiology ; Cognition Disorders ; etiology ; physiopathology ; Electroencephalography ; methods ; Event-Related Potentials, P300 ; physiology ; Humans ; Sleep Deprivation ; complications ; physiopathology

How to detect the P300 component in EEG accurately and instantly is a hot problem in the research field of Brain-Computer Interface. In this paper, an algorithm based on F-score feature selection and support vector machines was introduced for P300 detection. Using F-score feature selection method, we reduced input features to overcome the shortcoming of support vector machines in terms of low detection speed, and then implemented the detection of P300 component with support vector machines, which have good classification performance. The algorithm was tested with a P300 dataset from the BCI competition 2003. The results showed that the algorithm achieved an accuracy of 100% in P300 detection within five repetitions, and the detection speed of this algorithm was 2 times higher than that of the traditional support vector machines algorithm without F-score feature selection.
Algorithms ; Electroencephalography ; methods ; Event-Related Potentials, P300 ; physiology ; Humans ; Pattern Recognition, Automated ; methods ; Principal Component Analysis ; Signal Processing, Computer-Assisted