Juanhong Yu

Dynamic initiation and dual-tree complex wavelet feature-based classification of motor imagery of swallow EEG signals

The use of motor imagery-based brain computer interface has recently been shown to have potential for rehabilitation. This paper proposes a novel scheme to detect motor imagery of swallow from electroencephalography (EEG) signals for dysphagia rehabilitation. The proposed scheme extracts features from the coefficients of dual-tree complex wavelet transform (DT-CWT). A novel sliding window-based peak localization scheme is proposed to dynamically locate the initiation of tongue movement from Electromyography (EMG) signal. Subsequently, effective time segments are extracted from EEG signal for classification based on the detected dynamic initiation location. Comparisons are made between our proposed scheme with that of the three existing approaches. The results based on six healthy subjects show that an increase in averaged accuracy of 9.95% is achieved. Further, an increase in averaged accuracy of 8.02% is resulted comparing our proposed scheme by using and not using the dynamic initiation to extract the time segments. Classification results using EMG data confirm that our results are not due to movements artifacts. Statistical tests with 95% confidence to estimate the accuracy on the respective action at chance level show that five out of six subjects performed above chance level for our proposed dynamic initiation and wavelet feature-based approach.

Common frequency pattern for music preference identification using frontal EEG

In this paper, we investigate the use of 2-channel frontal EEG signal to classify two music preferences: like and dislike. The hypothesis for this investigation is that the frontal EEG signal contains sufficient information on the mental state of a subject for discriminating the preference of music of the subject. An experiment is performed to collect 2-channel frontal EEG data from 12 subjects by playing various types of music pieces and asking whether they like or dislike the music in order to obtain the true labels of their music preferences. We then propose a frequency band optimization method called common frequency pattern (CFP) for feature extraction and Linear SVM for classification to identify the music preference of the subjects from the 2-channel frontal EEG. The results of using the proposed method yield an average classification accuracy of 74.77% for a trial length of 30 s over the 12 subjects. Hence the experimental results show evidence that frontal EEG signal contains sufficient information to discriminate preference of music. Furthermore, the frequency band optimization results indicate that gamma band is essential for EEG-based music preference identification.

Extracting and selecting discriminative features from high density NIRS-based BCI for numerical cognition

Near-Infrared Spectroscopy (NIRS)-based Brain-Computer Interface (BCI) was recently studied for numerical cognition. This study presents a study using high density 348 channels NIRS-based BCI from 8 healthy subjects while solving mental arithmetic problems with two difficulty levels and the rest condition. The existing feature extraction and selection methods on the existing study were presented only for low density 16 channels NIRS-based BCI, and required the specification on the number of features to select to yield desirable performance. This paper presents a method of extracting discriminative features from high density single-trial NIRS data using common average reference spatial filtering and single-trial baseline reference, and a method of automatically selecting a set of discriminative and non-redundant features using the Mutual Information-based Rough Set Reduction (MIRSR) and Supervised Pseudo Self- Evolving Cerebellar (SPSEC) algorithms. The performance of the proposed method is evaluated using 5×5-fold cross-validations on the single-trial NIRS data collected using the support vector machine classifier. The results yielded an overall average accuracy of 71.4% and 91.0% in classifying hard versus easy tasks and hard versus rest tasks respectively using the proposed method, compared to 46.1% and 62.2% respectively using existing methods. The results demonstrated the effectiveness of using the proposed feature extraction and selection method in high density NIRS-based BCI for assessing numerical cognition.

Extracting effective features from high density nirs-based BCI for assessing numerical cognition

Near-infrared spectroscopy (NIRS)-based Brain-Computer Interface (BCI) was recently proposed to assess level of numerical cognition in subjects. However, existing feature extraction method was only proposed for low density 16 channels NIRS-based BCI. This study investigates the performance of a high density 348 channels NIRS-based BCI on 8 healthy subjects while they solve mental arithmetic problems with two difficulty levels and the rest condition. A novel method of extracting effective features from high density single-trial NIRS data is proposed using common average reference spatial filtering and single-trial baseline reference. The performance of the proposed feature extraction method is presented using 5×5-fold cross-validations on the single-trial NIRS data collected using mutual information-based feature selection and support vector machine classifier. The results yielded an overall average accuracy of 73% and 92% in classifying hard versus easy tasks and hard versus rest tasks respectively using the proposed method, compared to 46% and 62% respectively using existing method. The results demonstrated the effectiveness of using the proposed method in high density NIRS-based BCI for assessing numerical cognition.

A multimodal fNIRS and EEG-based BCI study on motor imagery and passive movement

In EEG-based motor imagery Brain-Computer interface (BCI), EEG data collected in the calibration phase is used as a subject-specific model to classify the EEG data in the evaluation phase. Previous study has shown the feasibility of calibrating EEG-based BCI from passive movement. This paper investigates the primary sensorimotor area activation from fNIRS on 4 subjects using multimodal NIRS and EEG-based BCI system while performing motor imagery and passive movement of the hand by a Haptic Knob robot. NIRS_SPM is used to compute the changes in hemoglobin response and to generate brain activation map based on the contrasts of motor imagery versus idle and passive movement versus idle. The results on the contrasts showed that passive movement versus idle yielded significant differences compared to motor imagery versus idle. In addition, the results of classifying the NIRS and EEG data separately also showed that the accuracies on classifying passive movement versus idle are better than that of motor imagery versus idle. The results suggest a potential of using passive movement data to calibrate motor imagery in a multimodal NIRS and EEG-based BCI.

Single-trial classification of NIRS data from prefrontal cortex during working memory tasks

This study presents single-trial classification performance on high density Near Infrared Spectroscopy (NIRS) data collected from the prefrontal cortex of 11 healthy subjects while performing working memory tasks and idle condition. The NIRS data collected comprised a total of 40 trials of n-back tasks for 2 difficulty levels: n=1 for easy and n=3 for hard. The single-trial classification was performed on features extracted using common average reference spatial filtering and single-trial baseline reference. The single-trial classification was performed using 5×5-fold cross-validations on the NIRS data collected by using mutual information-based feature selection and the support vector machine classifier. The results yielded average accuracies of 72.7%, 68.0% and 84.0% in classifying hard versus easy tasks, easy versus idle tasks and hard versus idle tasks respectively. Hence the results demonstrated a potential feasibility of using high density NIRS-based BCI for assessing working memory load.

Automatic Sleep Onset Detection Using Single EEG Sensor

Sleep has been shown to be imperative for the health and well-being of an individual. To design intelligent sleep management tools, such as the music-induce sleep-aid device, automatic detection of sleep onset is critical. In this work, we propose a simple yet accurate method for sleep onset prediction, which merely relies on Electroencephalogram (EEG) signal acquired from a single frontal electrode in a wireless headband. The proposed method first extracts energy power ratio of theta (4-8Hz) and alpha (8-12Hz) bands along a 3-second shifting window, then calculates the slow wave of each frequency band along the time domain. The resulting slow waves are then fed to a rule-based engine for sleep onset detection. To evaluate the effectiveness of the approach, polysomnographic (PSG) and headband EEG signals were obtained from 20 healthy adults, each of which underwent 2 sessions of sleep events. In total, data from 40 sleep events were collected. Each recording was then analyzed offline by a PSG technologist via visual observation of PSG waveforms, who annotated sleep stages N1 and N2 by using the American Academy of Sleep Medicine (AASM) scoring rules. Using this as the gold standard, our approach achieved a 87.5% accuracy for sleep onset detection. The result is better or at least comparable to the other state of the art methods which use either multi-or single- channel based data. The approach has laid down the foundations for our future work on developing intelligent sleep aid devices.

Cortical Activation of Passive Hand Movement Using Haptic Knob: a Preliminary Multi-channel fNIRS Study

Several functional neuroimaging studies had been performed to explore the sensorimotor function for motor imagery and passive movement, but there is scanty work that investigated the cortical activation pattern for passive movement using functional Near-Infrared Spectroscopy (fNIRS). This study investigated the cortical activation pattern from fNIRS data of 8 healthy subjects performing motor imagery and passive movement tasks using a Haptic Knob robot. Group averaged contrasts were defined as motor imagery versus idle and passive movement versus idle. The cortical activations for motor imagery appeared on the contralateral sensorimotor area, whereas the cortical activations for passive movement appeared on both contralateral and ipsilateral sensorimotor area. This result suggests that the performance of passive movement has a wider cortical activation compared to the performance of motor imagery.Several functional neuroimaging studies had been performed to explore the sensorimotor function for motor imagery and passive movement, but there is scanty work that investigated the cortical activation pattern for passive movement using functional Near-Infrared Spectroscopy (fNIRS). This study investigated the cortical activation pattern from fNIRS data of 8 healthy subjects performing motor imagery and passive movement tasks using a Haptic Knob robot. Group averaged contrasts were defined as motor imagery versus idle and passive movement versus idle. The cortical activations for motor imagery appeared on the contralateral sensorimotor area, whereas the cortical activations for passive movement appeared on both contralateral and ipsilateral sensorimotor area. This result suggests that the performance of passive movement has a wider cortical activation compared to the performance of motor imagery.

Reduction in Time-to-Sleep through EEG Based Brain State Detection and Audio Stimulation

We developed an EEG- and audio-based sleep sensing and enhancing system, called iSleep (interactive Sleep enhancement apparatus). The system adopts a closed-loop approach which optimizes the audio recording selection based on users sleep status detected through our online EEG computing algorithm. The iSleep prototype comprises two major parts: 1) a sleeping mask integrated with a single channel EEG electrode and amplifier, a pair of stereo earphones and a microcontroller with wireless circuit for control and data streaming; 2) a mobile app to receive EEG signals for online sleep monitoring and audio playback control. In this study we attempt to validate our hypothesis that appropriate audio stimulation in relation to brain state can induce faster onset of sleep and improve the quality of a nap. We conduct experiments on 28 healthy subjects, each undergoing two nap sessions - one with a quiet background and one with our audio-stimulation. We compare the time-to-sleep in both sessions between two groups of subjects, e.g., fast and slow sleep onset groups. The p-value obtained from Wilcoxon Signed Rank Test is 1.22e-04 for slow onset group, which demonstrates that iSleep can significantly reduce the time-to-sleep for people with difficulty in falling sleep.We developed an EEG- and audio-based sleep sensing and enhancing system, called iSleep (interactive Sleep enhancement apparatus). The system adopts a closed-loop approach which optimizes the audio recording selection based on users sleep status detected through our online EEG computing algorithm. The iSleep prototype comprises two major parts: 1) a sleeping mask integrated with a single channel EEG electrode and amplifier, a pair of stereo earphones and a microcontroller with wireless circuit for control and data streaming; 2) a mobile app to receive EEG signals for online sleep monitoring and audio playback control. In this study we attempt to validate our hypothesis that appropriate audio stimulation in relation to brain state can induce faster onset of sleep and improve the quality of a nap. We conduct experiments on 28 healthy subjects, each undergoing two nap sessions - one with a quiet background and one with our audio-stimulation. We compare the time-to-sleep in both sessions between two groups of subjects, e.g., fast and slow sleep onset groups. The p-value obtained from Wilcoxon Signed Rank Test is 1.22e-04 for slow onset group, which demonstrates that iSleep can significantly reduce the time-to-sleep for people with difficulty in falling sleep.