Prediction Model Based on BP-ANN and PCA for the Peak Occurrence of Liriomyza Huidobrensis

Liriomyza huidobrensis （Blanchard） is an important vegetable pest of pathology. In order to improve the accuracy of prediction of Liriomyza huidobrensis and to control the Liriomyza huidobrensis effectively, this paper presents a new prediction model by principal components analysis （PCA） and back propagation artificial neural network （BP-ANN） methods. The historical data from 1999 to 2007 on population occurrence are analyzed in order to find out a non-linear relationship between the pest occurrence and the meteorological factors. And then by using analysis results, the prediction model of Liriomyza huidobrensis occurrence in Jianshui in Yunnan is built. The new model has successfully applied to verify the paddy stem borer population occurrence in 2006. Test results show that the new prediction model with BP-ANN and PCA can improve the prediction accuracy.

Guest Editorial Brain-Computer Interface:Today and Tomorrow

As the invited editor of this special issue on braincomputer interface （BCI）, I am pleased to give a comment on the state-of-the-art with the introduction of recent advances made at the Chengdu BCI Group, University of Electronic Science and Technology of China （UESTC）.

Effect of Level of Processing Prior to Response on Cognitive Control:Evidence from ERP

Based on the idea that intentions have different penetrability to perception and thought, four Stroop-like tasks, AA, AW, WA, and WW are used. Event-related brain potentials are recorded as participants completed these tasks, and standardized low resolution brain electromagnetic tomography （sLORETA） is used to localize the sources at specific time points. These results show that there is an interference effect in the AA and WA tasks, but not in the AW or WW tasks. The activated brain areas related to the interference effect in the AA task are the PFC （prefrontal cortex） and ACC （anterior cingulated cortex）, and PFC aetivation takes place prior to ACC activation, but only in WA task. Combined with previous results, a new neural mechanism of cognitive control is proposed.

fMRI Study Revealing Neural Mechanisms of the Functions of SOA in Spatial Orienting

It is well documented that orienting attention plays an important role in visual search. However, it remains unclear how the executive brain regions will act when two different stimulus onset asynchrony （SOA） are used in visual search. In this work, event-related fMRI was used to investigate neural mechanisms on the functions of SOA in endogenous and exogenous orienting. The results showed that in the endogenous orienting, long SOA versus short SOA resulted in widespread cortical activation mainly including right medial frontal gyrus and bilateral middle frontal gyri. Conversely, in exogenous orienting, long SOA compared to short SOA resulted in only activations in bilateral middle frontal gyri. These findings indicated that these two spatial orienting involved different brain areas and neural mechanisms.

A Miniature Telemetric System for Freely Roaming Animals

Telemetric monitoring and control are the two critical aspects for a robot-rat. Development in this work is a telemetric system to record the electroencephalogram （EEG） from adult freely roaming animals. The system consists of two separated components： the transmit-end system, which consists of the preamplifier, the LPF （low-pass filter） and the transmitter, and the receive-end system, which consists of the receiver, the interface of receive-end and PC. The transmit-end system with light weight （10 g including battery） and small size （20 mm×50 mm） is fettered on the back of the rat. The EEG signal is modulated at the RF frequency of 2.4 GHz by nRF24E1 and transmitted by the antenna. The system can measure the EEG signal of the rat in freely roaming over a wireless transmission distance up to 8 m, and provide a new platform for behavioral and neurophysiological experiments.

A Study on SSVEP-Based BCI

-Brain-computer interface （BCI） can help the deformity person finish some basic activities. In this paper, we concern some critical aspects of SSVEP based BCI, including stimulator selection, method of SSVEP extracting in a short time, stimulating frequency selection, and signal electrode selection. The conclusion is that the stimulator type should be based on the complexity of the BCI system, the method based on wavelet analysis is more valid than the power spectrum method in extracting the SSVEP in a short period, and the selections of stimulating frequency and electrode are important in designing a BCI system. These contents are meaningful for implementing a real SSVEP-based BCI.

Scale-free brain ensemble modulated by phase synchronization

To listen to brain activity as a piece of music, we proposed the scale-free brainwave music(SFBM) technology, which could translate the scalp electroencephalogram(EEG) into music notes according to the power law of both EEG and music. In the current study, this methodology was further extended to a musical ensemble of two channels. First, EEG data from two selected channels are translated into musical instrument digital interface(MIDI) sequences, where the EEG parameters modulate the pitch, duration, and volume of each musical note. The phase synchronization index of the two channels is computed by a Hilbert transform. Then the two MIDI sequences are integrated into a chorus according to the phase synchronization index. The EEG with a high synchronization index is represented by more consonant musical intervals, while the low index is expressed by inconsonant musical intervals. The brain ensemble derived from real EEG segments illustrates differences in harmony and pitch distribution during the eyes-closed and eyes-open states. Furthermore, the scale-free phenomena exist in the brainwave ensemble. Therefore, the scale-free brain ensemble modulated by phase synchronization is a new attempt to express the EEG through an auditory and musical way, and it can be used for EEG monitoring and bio-feedback.

An ensemble with the Chinese pentatonic scale using electroencephalogram from both hemispheres

To listen to brain activity as a piece of music,we previously proposed scale-free brainwave music（SFBM）technology,which translated the scalp electroencephalogram（EEG）into musical notes according to the power law of both the EEG and music.In this study,the methodology was further extended to ensemble music on two channels from the two hemispheres.EEG data from two channels symmetrically located on the left and right hemispheres were translated into MIDI sequences by SFBM,and the EEG parameters modulated the pitch,duration and volume of each note.Then,the two sequences were filtered into an ensemble with two voices：the pentatonic scale（traditional Chinese music）or the heptatonic scale（standard Western music）.We demonstrated differences in harmony between the two scales generated at different sleep stages,with the pentatonic scale being more harmonious.The harmony intervals of this brain ensemble at various sleep stages followed the power law.Compared with the heptatonic scale,it was easier to distinguish the different stages using the pentatonic scale.These results suggested that the hemispheric ensemble can represent brain activity by variations in pitch,tempo and harmony.The ensemble with the pentatonic scale sounds more consonant,and partially reflects the relations of the two hemispheres.This can be used to distinguish the different states of brain activity and provide a new perspective on EEG analysis.