Biomedical subjects

Xiaorong Gao

Publications and source records attributed to Xiaorong Gao.

17 recordsLinked to original sources

PubMed2006

Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs.

Canonical correlation analysis (CCA) is applied to analyze the frequency components of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG). The essence of this method is to extract a narrowband frequency component of SSVEP in EEG. A recognition approach is proposed based on the extracted frequency features for an SSVEP-based brain computer interface (BCI). Recognition Results of the approach were higher than those using a widely used fast Fourier transform (FFT)-based spectrum estimation method.

Algorithms↗

PubMed2006

A practical VEP-based brain-computer interface.

This paper introduces the development of a practical brain-computer interface at Tsinghua University. The system uses frequency-coded steady-state visual evoked potentials to determine the gaze direction of the user. To ensure more universal applicability of the system, approaches for reducing user variation on system performance have been proposed. The information transfer rate (ITR) has been evaluated both in the laboratory and at the Rehabilitation Center of China, respectively. The system has been proved to be applicable to > 90% of people with a high ITR in living environments.

Adult↗

PubMed2006

Genotyping of Sporothrix schenckii by analysis of ribosomal DNA regions.

An attempt was made to identify the genotypes of 31 Sporothrix schenckii strains and the relationship between the genotypes, the geographic distributions and clinical manifestations using restriction fragment length polymorphic analysis. The total DNA was extracted by cetyltrimethyl ammonium bromide. The polymorphisms were detected by hybridisation of ApaI-digested Sporothrix genomic DNA with a probe amplified from the small subunit ribosomal DNA and adjacent internal transcribed spacer regions. The band patterns manifested by Southern blotting were explored to investigate the genotypes of the 31 strains of S. schenckii collected from five different areas in China. Of the data from the 31 strains of S. schenckii studied, 15 individual patterns (DNA type A-O) were recognised. Type A-C accounted for 51.61% of all the strains. The DNA typing of S. schenckii by Southern blotting was highly sensitive and highly distinguishable. We also found a significant correlation between DNA patterns and different geographic areas and clinical manifestations.

Blotting, Southern↗

PubMed2006

[Diagnosis and treatment under arthroscope in meniscus injury in middle and old aged people].

OBJECTIVE: To summarize the characteristic manifestations in the middle and old aged people with meniscus injury and the outcome of the treatment under the arthroscope. METHODS: Fifty-two patients, aged 52-58 years, with meniscus injury to a total of 57 knee joints, were diagnosed and treated under the arthroscope. The history of their knee diseases was 1-21 years. Horizontal tears occurred in 19 knee joints, degenerative tears in 13 knee joints, complex tears in 9 knee joints, longitudinal tears in 5 knee joints, oblique tears in 4 knee joints, radial tears in 4 knee joints, and flap tears in 3 knee joints. Three meniscus tears were sutured and 54 meniscus tears were cut fully or partly under the arthroscope. RESULTS: All the postoperative patients were followed up for 6-15 months, and the average follow-up period after operation was 9 months. According to the DONG Tianxiang's standards for the therapy under the arthroscope, the excellent result was achieved in 39 knee joints, good in 12 knee joints, and fair in 6 knee joints, with no failure. The excellent and good rate was 89.5%. CONCLUSION: The clinical manifestations of meniscus injury are not typical in the middle and old aged people. The therapeutic effect with the help of the arthroscope is satisfactory with an advantage of minimal traumatic invasiveness to the knee joint.

Aged↗

PubMed2006

[The sleep staging based on HRV analysis].

In order to deduce the sleep stages from heart rate, we analyze the heart rate variability (HRV) with hidden Markov model (HMM) for the identification of different characters of HRV within different sleep stages. Special technique is used to compensate the individual diversity. The relationship between the sleep stage and the ultra-low frequency components of HRV is also considered. Since the detection of heart rate hardly disturbs the sleep, the proposed method provides a simple approach to evaluating the sleep stage without disturbing the sleep. Our experiments have proved that this method meets the requirements of wide applications, especially the requirement of routine use in monitoring the normal subjects' sleep.

Adult↗

PubMed2005

An epileptic seizure prediction algorithm based on second-order complexity measure.

The quality of life of many epilepsy patients may be improved significantly if the occurrence of epileptic seizures can be successfully forecasted and clinical intervention, such as electrical stimulation or drug delivery, can then be used to suppress their emergence, or warn the patient of the forthcoming events. In this paper, a prediction algorithm based on the second-order complexity measure was proposed to predict the impending seizures. Through the analysis of long-term intracranial EEG recordings from two frontal lobe epilepsy patients, the results indicated that the sensitivity of prediction was 77.8% (14/18) and 66.7% (4/6) and the number of false warnings was 3 and 2 for the two patients, respectively. Because only the information of past seizures was utilized to predict the current seizure and the computation load was low, the prediction algorithm could possibly be applied to clinical practice.

Adolescent↗

PubMed2005

The relationship of HRV to sleep EEG and sleep rhythm.

Previous studies have shown that there exists a cycle of NREM (non-rapid eye movement)-REM (rapid eye movement) during normal human sleep, and heart rate variability (HRV) has a close relationship to sleep stages and sleep cycle. This article reports the relationship between the electroencephalographic activity and the HRV spectral power in several specific frequency bands. The authors discovered that relationships do exist between HRV and electroencephalogram (EEG) during sleep. In particular, it was found that, prior to the changes of EEG, the changes of HRV usually indicate the shift of sleep stages. HRV frequency analysis indicates that the very-low-frequency components of HRV are closely related to sleep EEG. Results show that the rhythm of the spectral power oscillations in some specific frequency bands of HRV is almost the same as the sleep cycle, which reflects the rhythm of sleep to a certain extent.

Adolescent↗

PubMed2005

Standardized shrinking LORETA-FOCUSS (SSLOFO): a new algorithm for spatio-temporal EEG source reconstruction.

This paper presents a new algorithm called Standardized Shrinking LORETA-FOCUSS (SSLOFO) for solving the electroencephalogram (EEG) inverse problem. Multiple techniques are combined in a single procedure to robustly reconstruct the underlying source distribution with high spatial resolution. This algorithm uses a recursive process which takes the smooth estimate of sLORETA as initialization and then employs the re-weighted minimum norm introduced by FOCUSS. An important technique called standardization is involved in the recursive process to enhance the localization ability. The algorithm is further improved by automatically adjusting the source space according to the estimate of the previous step, and by the inclusion of temporal information. Simulation studies are carried out on both spherical and realistic head models. The algorithm achieves very good localization ability on noise-free data. It is capable of recovering complex source configurations with arbitrary shapes and can produce high quality images of extended source distributions. We also characterized the performance with noisy data in a realistic head model. An important feature of this algorithm is that the temporal waveforms are clearly reconstructed, even for closely spaced sources. This provides a convenient way to estimate neural dynamics directly from the cortical sources.

Action Potentials↗

PubMed2004

Mu rhythm-based cursor control: an offline analysis.

OBJECTIVE: To classify the EEG data recorded in mu rhythm-based cursor control experiments with 4 possible choices. METHODS: The algorithm included preprocessing, feature extraction, and classification. Two spatial filters, common average reference and common spatial subspace decomposition, were used in preprocessing to improve the signal-to-noise ratio, and then two features were extracted based on the power spectrum and the time course of the mu rhythm respectively. A Fisher ratio was defined to select channels in feature extraction. A 2-dimensional linear classifier was trained for final classification. RESULTS: Two types of classifiers were trained for the training dataset. The uniform classifier gave a classification accuracy of 76.4%, and the classifier trained by leave-one-out method gave a classification accuracy of 74.4%, both higher than the online accuracy 69.5%. The uniform classifier was applied to the test dataset and the classification accuracy was 65.9%, lower than the online accuracy 73.2%. CONCLUSIONS: Spatial filtering can give a notable improvement in classification accuracy. The time course of the mu rhythm, as well as the power of the mu rhythm, shows difference between the 4 targets, and can contribute to the classification. SIGNIFICANCE: The spatial filtering, feature extraction and channel selection methods in the algorithm will provide some practical suggestions for further study on the mu rhythm-based brain-computer interface.

Algorithms↗

PubMed2004

Classification of single-trial electroencephalogram during finger movement.

We present an algorithm to discriminate between the single-trial electroencephalograms (EEG) of two different finger movement tasks. The method uses a spatio-temporal analysis to classify the EEG recorded during voluntary left versus right finger movement tasks. This algorithm produced a classification accuracy of 92.1% on the data from five subjects, without requiring subject training or data selection. This technique can be employed in an EEG-based brain-computer interface due to its high recognition rate, insensitivity to noise, and simplicity in computation.

Adult↗

PubMed2004

BCI Competition 2003--Data set IV: an algorithm based on CSSD and FDA for classifying single-trial EEG.

This paper presents an algorithm for classifying single-trial electroencephalogram (EEG) during the preparation of self-paced tapping. It combines common spatial subspace decomposition with Fisher discriminant analysis to extract features from multichannel EEG. Three features are obtained based on Bereitschaftspotential and event-related desynchronization. Finally, a perceptron neural network is trained as the classifier. This algorithm was applied to the data set (self-paced 1s) of "BCI Competition 2003" with a classification accuracy of 84% on the test set.

Algorithms↗

PubMed2004

BCI Competition 2003--Data set IIb: enhancing P300 wave detection using ICA-based subspace projections for BCI applications.

An algorithm based on independent component analysis (ICA) is introduced for P300 detection. After ICA decomposition, P300-related independent components are selected according to the a priori knowledge of P300 spatio-temporal pattern, and clear P300 peak is reconstructed by back projection of ICA. Applied to the dataset IIb of BCI Competition 2003, the algorithm achieved an accuracy of 100% in P300 detection within five repetitions.

Algorithms↗

PubMed2004

A recursive algorithm for the three-dimensional imaging of brain electric activity: Shrinking LORETA-FOCUSS.

Estimation of intracranial electric activity from the scalp electroencephalogram (EEG) requires a solution to the EEG inverse problem, which is known as an ill-conditioned problem. In order to yield a unique solution, weighted minimum norm least square (MNLS) inverse methods are generally used. This paper proposes a recursive algorithm, termed Shrinking LORETA-FOCUSS, which combines and expands upon the central features of two well-known weighted MNLS methods: LORETA and FOCUSS. This recursive algorithm makes iterative adjustments to the solution space as well as the weighting matrix, thereby dramatically reducing the computation load, and increasing local source resolution. Simulations are conducted on a 3-shell spherical head model registered to the Talairach human brain atlas. A comparative study of four different inverse methods, standard Weighted Minimum Norm, L1-norm, LORETA-FOCUSS and Shrinking LORETA-FOCUSS are presented. The results demonstrate that Shrinking LORETA-FOCUSS is able to reconstruct a three-dimensional source distribution with smaller localization and energy errors compared to the other methods.

Algorithms↗

PubMed2004

[The progress in epileptic seizure prediction].

It is estimated that epilepsy, a chronic disorder of the nervous system, affects about 0.5%-2% of the population and about 10%-50% do not respond well to current antiepileptic medications and may not be candidates for surgery. For these patients, the unpredictability of seizure onset is a major cause of disability and mortality. Therefore, anticipation of an imminent seizure would be beneficial to patients because it could provide time for the application of preventive measures to keep the risk of seizure to a minimum. This paper reviews the feasibilities, the progress, existing problems and possible applications in the field of epileptic seizure prediction.

Algorithms↗

PubMed2003

A BCI-based environmental controller for the motion-disabled.

With the development of brain-computer interface (BCI) technology, researchers are now attempting to put current BCI techniques into practical application. This paper presents an environmental controller using a BCI technique based on steady-state visual evoked potential. The system is composed of a stimulator, a digital signal processor, and a trainable infrared remote-controller. The attractive features of this system include noninvasive signal recording, little training requirement, and a high information transfer rate. Our test results have shown that this system can distinguish at least 48 targets and provide a transfer rate up to 68 b/min. The system has been applied to the control of an electric apparatus successfully.

Algorithms↗

PubMed2002

Design and implementation of a brain-computer interface with high transfer rates.

This paper presents a brain-computer interface (BCI) that can help users to input phone numbers. The system is based on the steady-state visual evoked potential (SSVEP). Twelve buttons illuminated at different rates were displayed on a computer monitor. The buttons constituted a virtual telephone keypad, representing the ten digits 0-9, BACKSPACE, and ENTER. Users could input phone number by gazing at these buttons. The frequency-coded SSVEP was used to judge which button the user desired. Eight of the thirteen subjects succeeded in ringing the mobile phone using the system. The average transfer rate over all subjects was 27.15 bits/min. The attractive features of the system are noninvasive signal recording, little training required for use, and high information transfer rate. Approaches to improve the performance of the system are discussed.

Activities of Daily Living↗

PubMed2002

[Temporal-spatial analysis of evoked potentials].

Evoked potentials are widely used in clinical neurophysiology. The conventional analysis methods of evoked potentials are based on the waves in time domain. Analysis based on time-spatial domain will provide more information than simple time domain analysis. The existing temporal-spatial analysis methods, such as microstate, frequency domain analysis and event-related coherence, are introduced in this paper.

Evoked Potentials↗