Xingyu Wang

An Automatic Spike Detection System Based on Elimination of False Positives Using the Large-Area Context in the Scalp EEG

Most automatic spike detection systems in the scalp electroencephalogram (EEG) focused on the characteristics of “spike.” However, the characteristics of “false positives” (FPs) have not been fully studied. In this paper, we proposed a system that contains a series of algorithms to eliminate FPs and a template method to confirm spikes. The system used large area context available on 49 channels from two common montages. The impact of slow-waves after spikes was taken into consideration, as well as the information from single channel, multichannel, and whole recording. Two types of FPs were identified in this paper. The ones from typical artifacts were identified by analysis of background EEG activities, and the ones from other EEG activities were declared by spatial and temporal characteristics of spike activities. Finally, a multichannel template method was used to assess the performance of the proposed system. The system was evaluated using 17 routine EEG recordings. Spike activities were observed in six of them. Effective multichannel templates were extracted from four recordings containing frequent spikes. The least selectivity was 92.6% and the most false positive rate was 0.26 min-1. Proposed algorithms for elimination of FPs are also suitable for other algorithms to enhance performance since most FPs can be identified while few true spikes are eliminated.

Human Intention Extracted from Electromyography Signals for Tracking Motion of Meal Assistance Robot

The objective of the present study is to determine if surface electromyography (EMG) signals can be used for controlling the motion of the meal assistance robot. The power of four EMG channels were calculated and encoded into five commands to track the motion of meal assistance robot. The results indicated that by using EMG signals, the meal assistance robot was able to follow the movement desire of the subjects and help them to eat. This study has shown the possibility of extracting the features of EMG signals that reflect the subjects intent as control signals.

Automatic reference selection for quantitative EEG interpretation: identification of diffuse/localised activity and the active earlobe reference, iterative detection of the distribution of EEG rhythms.

EEG (Electroencephalograph) interpretation is important for the diagnosis of neurological disorders. The proper adjustment of the montage can highlight the EEG rhythm of interest and avoid false interpretation. The aim of this study was to develop an automatic reference selection method to identify a suitable reference. The results may contribute to the accurate inspection of the distribution of EEG rhythms for quantitative EEG interpretation. The method includes two pre-judgements and one iterative detection module. The diffuse case is initially identified by pre-judgement 1 when intermittent rhythmic waveforms occur over large areas along the scalp. The earlobe reference or averaged reference is adopted for the diffuse case due to the effect of the earlobe reference depending on pre-judgement 2. An iterative detection algorithm is developed for the localised case when the signal is distributed in a small area of the brain. The suitable averaged reference is finally determined based on the detected focal and distributed electrodes. The presented technique was applied to the pathological EEG recordings of nine patients. One example of the diffuse case is introduced by illustrating the results of the pre-judgements. The diffusely intermittent rhythmic slow wave is identified. The effect of active earlobe reference is analysed. Two examples of the localised case are presented, indicating the results of the iterative detection module. The focal and distributed electrodes are detected automatically during the repeating algorithm. The identification of diffuse and localised activity was satisfactory compared with the visual inspection. The EEG rhythm of interest can be highlighted using a suitable selected reference. The implementation of an automatic reference selection method is helpful to detect the distribution of an EEG rhythm, which can improve the accuracy of EEG interpretation during both visual inspection and automatic interpretation.

Automatic interpretation of hyperventilation-induced electroencephalogram constructed in the way of qualified electroencephalographer’s visual inspection

Quantitative analysis and detection of electroencephalogram (EEG) recordings during evoked activities is essential for clinical diagnosis on neurological disorders. However, the process of interpreting EEG is time consuming for electroencephalographers (EEGers). In this study, an automatic EEG interpretation system constructed in the way of qualified EEGer’s visual inspection was proposed. The system was applied to interpret hyperventilation-induced EEG automatically. The final results of automatic interpretation were compared with EEGer’s visual inspection, and showed high consistence.

Automatic adaptive onset detection using an electromyogram with individual difference for control of a meal assistance robot

The onset detection of muscle activation is an essential issue in electromyogram (EMG) control. In this paper, a novel approach based on EMG power with automatic adaptive threshold is proposed to address this issue. The purpose is to develop an effective EMG-controlled meal assistance robot. Taking into account the individual difference such as contraction power and resting power, the threshold of onset detection is set with respect to the latest EMG signal. The results show the method is able to adjust automatically to avoid false alarms, and works well when the contraction power varies. Implementation of this EMG-controlled meal assistance robot may provide limb-deficient patients with an effective and comfortable human–machine assistance interface.

Conditional probability of Cauchy distribution in automatic sleep stage determination for sleep data with artifacts

An automatic sleep stage determination system dealing with the sleep data contaminated by artifacts is developed, which is working on an expert knowledge-based multi-valued decision making method. The knowledge database is consisted of probability density functions of parameters for various sleep stages according to the visual inspection by a qualified clinician. The probability density functions are approximated by Cauchy distribution on histograms. Sleep stages are determined automatically according to the maximum value of conditional probability. Due to the infinite variance of Cauchy distribution, the effect of mis-determination caused by artifacts can be abated. The result of automatic sleep stage determination was satisfactory. The presented automatic sleep stage determination can be an assistant tool for clinical practice.

Quantitative Evaluation of Spiral Drawing by Patients with Movement Disorder based on Polar Coordinate System with Varied Origin

In this study, quantitative evaluation of spiral drawing by patients with movement disorder was investigated in order to extract characteristics of hand movement disabilities. The hand movement of circular spiral drawing was recorded by a digitized tablet. A method of polar coordinate system with varied origin was proposed for evaluation. A set of characteristic parameters were also defined and calculated. Compare with fixed origin, the variation of radius, origin and degree can be evaluated quantitatively. The proposed method of polar coordinate with varied origin is effective to help clinicians in diagnosing the diseases and inspecting the effectiveness of operation and treatment.