Yu Qi

• PhD
• Junior Principle Investigator at Zhejiang University

How the human motor cortex (MC) orchestrates sophisticated sequences of fine movements such as handwriting remains a puzzle. Here we investigate this question through Utah array recordings from human MC during attempted handwriting of Chinese characters (n = 306, each consisting of 6.3 ± 2.0 strokes). We find that MC activity evolves through a sequ...

Fine movements of hands play an important role in everyday life. While existing studies have successfully decoded hand gestures or finger movements from brain signals, direct decoding of single-joint kinematics remains challenging. This study aims to investigate the decoding of fine hand movements from a single-joint level. Neural activities were r...

Handwriting Brain-Computer Interfaces (BCIs) provides a promising communication avenue for individuals with paralysis. While English-based handwriting BCIs have achieved rapid typewriting with 26 lowercase letters (mostly containing one stroke each), it is difficult to extend to complex characters, especially those with multiple strokes and large c...

How our brain encodes complex concepts has been a longstanding mystery in neuroscience. The answer to this problem can lead to new understandings about how the brain retrieves information in large-scale data with high efficiency and robustness. Neuroscience studies suggest the brain represents concepts in a locality-sensitive hashing (LSH) strategy...

Brain-computer interfaces (BCIs) have enabled prosthetic device control by decoding motor movements from neural activities. Neural signals recorded from cortex exhibit nonstationary property due to abrupt noises and neuroplastic changes in brain activities during motor control. Current state-of-the-art neural signal decoders such as Kalman filter a...

Neural decoding, which transforms neural signals into motor commands, plays a key role in brain-computer interfaces (BCIs). Existing neural decoding approaches mainly rely on the assumption of independent noises, which could perform poorly in case the assumption is invalid. However, correlations in noises have been commonly observed in neural signa...

Denoising diffusion probabilistic models (DDPMs) have gained popularity in devising neural vocoders and obtained outstanding performance. However, existing DDPM-based neural vocoders struggle to handle the prosody diversities due to their susceptibility to mode-collapse issues confronted with imbalanced data. We introduced Cauchy Diffusion, a model...

Objective: Speech brain-computer interfaces (speech BCIs), which convert brain signals into spoken words or sentences, have demonstrated great potential for high-performance BCI communication. Phonemes are the basic pronunciation units. For monosyllabic languages such as Chinese Mandarin, where a word usually contains less than three phonemes, accu...

In motor cortex, behaviorally relevant neural responses are entangled with irrelevant signals, which complicates the study of encoding and decoding mechanisms. It remains unclear whether behaviorally irrelevant signals could conceal some critical truth. One solution is to accurately separate behaviorally relevant and irrelevant signals at both sing...

Deep brain stimulation (DBS) targeting the lateral habenula (LHb) is a promising therapy for treatment-resistant depression (TRD) but its clinical effect has been variable, which can be improved by adaptive DBS (aDBS) guided by a neural biomarker of depression symptoms. A clinically-viable neural biomarker is desired to classify depression symptom...

In motor cortex, behaviorally-relevant neural responses are entangled with irrelevant signals, which complicates the study of encoding and decoding mechanisms. It remains unclear whether behaviorally-irrelevant signals could conceal some critical truth. One solution is to accurately separate behaviorally-relevant and irrelevant signals at both sing...

The identification of prototypical waveforms, such as sleep spindles and epileptic spikes, is crucial for the diagnosis of neurological disorders. These prototypical waveforms are usually recurrently presented in certain brain states, serving as potential biomarkers for clinical evaluations. Convolutional sparse coding (CSC) approaches have demonst...

The assessment of consciousness states, especially distinguishing minimally conscious states (MCS) from unresponsive wakefulness states (UWS), constitutes a pivotal role in clinical therapies. Despite that numerous neural signatures of consciousness have been proposed, the effectiveness and reliability of such signatures for clinical consciousness...

How the human motor cortex (MC) orchestrates sophisticated fine movements such as handwriting remains a puzzle 1–3 . Here, we investigate this question through Utah array recordings from human MC hand knob, during imagined handwriting of Chinese characters (306 characters tested, 6.3 ± 2.0 strokes per character). We find MC programs the writing of...

In motor cortex, behaviorally-relevant neural responses are entangled with irrelevant signals, which complicates the study of encoding and decoding mechanisms. It remains unclear whether behaviorally-irrelevant signals could conceal some critical truth. One solution is to accurately separate behaviorally-relevant and irrelevant signals, but this ap...

Objective. Spike sorting, a critical step in neural data processing, aims to classify spiking events from single electrode recordings based on different waveforms. This study aims to develop a novel online spike sorter, NeuSort, using neuromorphic models, with the ability to adaptively adjust to changes in neural signals, including waveform deforma...

Brain-computer interfaces (BCIs) provide a direct pathway from the brain to external devices and have demonstrated great potential for assistive and rehabilitation technologies. Endogenous BCIs based on electroencephalogram (EEG) signals, such as motor imagery (MI) BCIs, can provide some level of control. However, mastering spontaneous BCI control...

Brain-computer interfaces (BCIs) provide a direct pathway from the brain to external devices and have demonstrated great potential for assistive and rehabilitation technologies. Endogenous BCIs based on electroencephalogram (EEG) signals, such as motor imagery (MI) BCIs, can provide some level of control. However, mastering spontaneous BCI control...

Biometric features, e.g., fingerprints, the iris, and the face, have been widely used to authenticate individuals. However, most biometrics are not cancellable, i.e., once these biometric features are cloned or stolen, they cannot be replaced easily. Unlike traditional biometrics, brain biometrics are extremely difficult to clone or forge due to th...

In motor cortex, behaviorally-relevant neural responses are entangled with irrelevant signals, which complicates the study of encoding and decoding mechanisms. It remains unclear whether behaviorally-irrelevant signals could conceal some critical truth. One solution is to accurately separate behaviorally-relevant and irrelevant signals, but this ap...

Biometrics, e.g., fingerprints, the iris, and the face, have been widely used to authenticate individuals. However, most biometrics are not cancellable, i.e., once these traditional biometrics are cloned or stolen, they cannot be replaced easily. Unlike traditional biometrics, brain biometrics are extremely difficult to clone or forge due to the na...

Brain-computer Interface (BCI) builds a neural signal to the motor command pathway, which is a prerequisite for the realization of neural prosthetics. However, a long-term stable BCI suffers from the neural data drift across days while retraining the BCI decoder is expensive and restricts its application scenarios. Recent solutions of neural signal...

In motor cortex, behaviorally-relevant neural responses are entangled with irrelevant signals, which complicates the study of encoding and decoding mechanisms. It remains unclear whether behaviorally-irrelevant signals could conceal some critical truth. One solution is to accurately separate behaviorally-relevant and irrelevant signals, but this ap...

Speech brain-computer interfaces (BCIs), which translate brain signals into spoken words or sentences, have shown significant potential for high-performance BCI communication. Phonemes are the fundamental units of pronunciation in most languages. While existing speech BCIs have largely focused on English, where words contain diverse compositions of...

Brain-machine interfaces (BMI) have developed rapidly in recent years, but still face critical issues such as accuracy and stability. Ideally, a BMI system would be an implantable neuroprosthesis that would be tightly connected and integrated into the brain. However, the heterogeneity of brains and machines hinders deep fusion between the two. Neur...

Bandpass filters play a core role in ECoG signal processing. Commonly used frequency bands such as alpha, beta, and gamma bands can reflect the normal rhythm of the brain. However, the universally predefined bands might not be optimal for a specific task. Especially the gamma band usually covers a wide frequency span (i.e., 30–200 Hz) which can be...

Spike sorting, which classifies spiking events of different neurons from single electrode recordings, is an essential and widely used step in neural data processing and analysis. The recent development of brain-machine interfaces enables online control of external devices and closed-loop neuroprosthetics using single-unit activity, making online sp...

Spiking Neural Networks (SNNs) are biologically realistic and practically promising in low-power computation because of their event-driven mechanism. Usually, the training of SNNs suffers accuracy loss on various tasks, yielding an inferior performance compared with ANNs. A conversion scheme is proposed to obtain competitive accuracy by mapping tra...

Consciousness detection is important in diagnosis and treatment of disorders of consciousness (DOC). Recent studies have demonstrated that electroencephalography (EEG) signals contain effective information for consciousness state evaluation. We propose two novel EEG measures: the spatiotemporal correntropy and the neuromodulation intensity, to refl...

Fabric defect detection has been an important but challenging problem. One crucial problem lies in that the fabric colors and textures highly affect the detection performance. Especially, defects with low contrast to the fabric patterns are difficult to detect. We find that the color space is one important issue, and defects exhibit diverse discrim...

Invasive brain-computer interfaces (iBCIs) have demonstrated great potential in neural function restoration by decoding intention from brain signals for external device control. Spike trains and local field potentials (LFPs) are two typical intracortical neural signals with good complementarity from time and frequency domains. However, existing stu...

Understanding how motor cortex encodes and decodes behaviors is a fundamental goal of neuroscience, which faces significant challenges because taskrelevant neural responses are entangled with considerable task-irrelevant nuisance signals. Due to the entanglement, it is difficult to determine to extract which part and how much neural activity to stu...

Deep neural networks provide end-to-end tools to learn effective representations from data directly. The deep structure makes it possible to model a complicated pattern, even if it has a variety of changes. This leads to a problem that noises and outliers are usually treated as a specific pattern, which is also learned in the network. It is one rea...

Objective: Brain-machine interfaces (BMIs) aim to provide direct brain control of devices such as prostheses and computer cursors, which have demonstrated great potential for motor restoration. One major limitation of current BMIs lies in the unstable performance due to the variability of neural signals, especially in online control, which serious...

We present an implantable brain-computer interface surgical case assisted by robotic navigation system in an elderly patient with tetraplegia caused by cervical spinal cord injury. Left primary motor cortex was selected for implantation of microelectrode arrays based on fMRI location of motor imagery. Robotic navigation system was used during this...

Objective: Brain-machine interfaces (BMIs) aim to provide direct brain control of devices such as prostheses and computer cursors, which have demonstrated great potential for mobility restoration. One major limitation of current BMIs lies in the unstable performance in online control due to the variability of neural signals, which seriously hinders...

Sleep spindles are closely associated with cognitive functions and neurological disorders; thus, spindle detection has been an important topic in sleep medicine. Recently, machine learning approaches have shown the potential in automatic sleep spindle detection by learning optimized features in a data-driven way, while they highly rely on labeled d...

Neuroprosthesis refers to implantable medical devices which can replace injured biological functions in the brain. One of the core problems in neuroprosthesis study is to construct a neural signal transformation model from one cortical area to another. Since the brain encodes and transmits information in spike trains, spiking neural network (SNN) c...

Traditional biometrics such as face, iris and fingerprint have been applied widely nowadays. Nevertheless, with more and more potential problems being exposed, such as privacy leak and fabricate attack, it is urgent to find new secure biometrics to meet the needs. Identification based on brain signals is a promising option due to its unique advanta...

Information transmission security is an important issue in many scenarios such as password input. Traditional approaches such as typing or voice input are prone to peep, leading to a risk of information leakage. Brain computer interface (BCI) can read information directly from the brain, which is confidential inherently, thus it may be an ideal way...

Objective. Brain–machine interfaces (BMIs) provide a direct pathway between the brain and external devices such as computer cursors and prosthetics, which have great potential in motor function restoration. One critical limitation of current BMI systems is the unstable performance, partly due to the variability of neural signals. Studies showed tha...

Objective: Brain-computer interfaces (BCI) that enables people with severe motor disabilities to use their brain signals for direct control of objects have attracted increased interest in rehabilitation. To date, no study has investigated feasibility of the BCI framework incorporating both intracortical and scalp signals. Methods: Concurrent local...

Seizure prediction from intracranial electroencephalogram (iEEG) has great potentials to improve the life quality of epileptic patients, but faces big challenges. One major difficulty lies in that brain signal changes occasionally during long-term monitoring, due to electrode movements or the nonstationary brain dynamics. This leads to a serious si...

Dimensionality reduction plays an important role in neural signal analysis. Most dimensionality reduction methods can effectively describe the majority of the variance of the data, such as principal component analysis (PCA) and locally linear embedding (LLE). However, they may not be able to capture useful information given a specific task, since t...

Reconstructing seeing images from fMRI recordings is an absorbing research area in neuroscience and provides a potential brain-reading technology. The challenge lies in that visual encoding in brain is highly complex and not fully revealed. Inspired by the theory that visual features are hierarchically represented in cortex, we propose to break the...

Concealed information detection has a strong connection with human cognition. Existing deception detection approaches such as polygraph testing usually exploit psychophysiological changes. However, they often suffer from low accuracy and attacking-by-training. Compared with conventional physiological responses, electroencephalogram (EEG) directly r...

In this paper, we consider sequential online prediction (SOP) for streaming data in the presence of outliers and change points. We propose an INstant TEmporal structure Learning (INTEL) algorithm to address this problem. Our INTEL algorithm is developed based on a full consideration of the duality between online prediction and anomaly detection. We...

Objective. Brain-computer interface (BCI) has demonstrated its effectiveness in epilepsy treatment and control. In a BCI-aided epilepsy treatment system, therapic electrical stimulus is delivered in response to the prediction of upcoming seizure onsets, therefore timely and accurate seizure prediction algorithm plays an important role. However, unl...

Responsive neurostimulation (RNS) is becoming a promising therapy in refractory epilepsy control. In a RNS system, a critical challenge is how to detect seizure onsets accurately with low computational costs. In this study, an energy efficient AdaBoost cascade method for robust long-term seizure detection from local field potential (LFP) signals wa...

Objective: Brain network connectivity analysis plays an important role in computer-aided automatic localization of seizure onset zone (SOZ) from Intracranial Electroencephalography (iEEG). However, how to accurately compute brain network dynamics is still not well addressed. This work aims to develop an effective measure to find out the dynamics f...

Web attacks such as Cross-Site Scripting and SQL Injection are serious Web threats that lead to catastrophic data leaking and loss. Because attack payloads are often short segments hidden in URL requests/posts that can be very long, classical machine learning approaches have difficulties in learning useful patterns from them. In this study, we prop...

In this paper, we consider sequential online prediction (SOP) for streaming data in the presence of outliers and change points. We propose an INstant TEmporal structure Learning (INTEL) algorithm to address this problem.Our INTEL algorithm is developed based on a full consideration to the duality between online prediction and anomaly detection. We...

In this paper, we consider sequential online prediction (SOP) for streaming data in the presence of outliers and change points. We propose an INstant TEmporal structure Learning (INTEL) algorithm to address this problem. Our INTEL algorithm is developed based on a full consideration of the duality between online prediction and anomaly detection. We...

Activity-dependent plasticity plays an important role in biological neural network learning. Unlike the backpropagation-based learning in artificial neural networks that depends on supervised signals, biological neurons adjust themselves using their own historical behaviors as a clue to benefit information processing. Inspired by this biological ne...

Electronic noses recognize odors using sensor arrays, and usually face difficulties for odor complicacy, while animals have their own biological sensory capabilities for various types of odors. By implanting electrodes into the olfactory bulb of mammalian animals, odors may be recognized by decoding the recorded neural signals, in order to construc...

Brain-computer interface (BCI) is a direct communication pathway between brain and external devices, and BCI-based prosthetic devices are promising to provide new rehabilitation options for people with motor disabilities. Electrocorticography (ECoG) signals contain rich information correlated with motor activities, and have great potential in hand...

Spiking neural networks (SNNs) are considered to be biologically plausible and power-efficient on neuromorphic hardware. However, unlike the brain mechanisms, most existing SNN algorithms have fixed network topologies and connection relationships. This paper proposes a method to jointly learn network connections and link weights simultaneously. The...

Spiking Neural Networks (SNNs) represent and transmit information in spikes, which is considered more biologically realistic and computationally powerful than the traditional Artificial Neural Networks. The spiking neurons encode useful temporal information and possess highly anti-noise property. The feature extraction ability of typical SNNs is li...

Objective: Computer-aided estimation of brain connectivity aims to reveal information propagation in brain automatically, which has great potential in clinical applications, e.g. epilepsy foci diagnosis. Granger causality is an effective tool for directional connection analysis in multivariate time series. However, most existing methods based on G...

Objective. There is serious noise in EEG caused by eye blink and muscle activities. The noise exhibits similar morphologies to epileptic seizure signals, leading to relatively high false alarms in most existing seizure detection methods. The objective in this paper is to develop an effective noise suppression method in seizure detection and explore...

Throughout the history, insects had been intimately connected to humanity, in both positive and negative ways. Insects play an important part in crop pollination, on the other hand, some of them spread diseases that kill millions of people every year. Effective control of harmful insects while having little impact to beneficial insects and environm...

This article proposes a state-space model with Cauchy observation noise (SSMC) to detect seizure onset in a long-term EEG monitoring system. Facing the challenge of high false detection rates (FDRs) in many existing methods caused by impulsive EOG/EMG artifacts, the SSMC uses a nonlinear state-space model to encode the gradual changes of epileptic...

Brain-computer interfaces (BCIs) can provide direct bidirectional communication between the brain and a machine. Recently, the BCI technique has been used in seizure control. Usually, a closed-loop system based on BCI is set up which delivers a therapic electrical stimulus only in response to seizure onsets. In this way, the side effects of neurost...

Motor brain-machine interface (BMI) has great potentials in neural motor prostheses and has received increasing attention during the past decades in the neural engineering field. It requires an approach to decode neural activities that represents desired movements. Much of the progress in decoding algorithms has been driven by the availability of n...

Objective This study presents a multichannel patient-specific seizure detection method based on the empirical mode decomposition (EMD) and support vector machine (SVM) classifier.Materials and Methods The EMD is used to extract features from intracranial electroencephalography (EEG). A machine-learning algorithm is used as a classifier to discrimin...

Effective seizure detection from long-term EEG is highly important for seizure diagnosis. Existing methods usually design the feature and classifier individually, while little work has been done for the simultaneous optimization of the two parts. This work proposes a deep network to jointly learn a feature and a classifier so that they could help e...

Unsupervised feature learning with deep networks has been widely studied in the recent years. Despite the progress, most existing models would be fragile to non-Gaussian noises and outliers due to the criterion of mean square error (MSE). In this paper, we propose a robust stacked autoencoder (R-SAE) based on maximum correntropy criterion (MCC) to...

Seizure detection from electroencephalogram (EEG) plays an important role for epilepsy therapy. Due to the diversity of seizure EEG patterns between different individuals, multiple features are necessary for high accuracy since a single feature could hardly encode all types of epileptiform discharges. However, a large feature set inevitably causes...

Active rehabilitation involves patient's voluntary thoughts as the control signals of restore device to assist stroke rehabilitation. Although restoration of hand opening stands importantly in patient's daily life, it is difficult to distinguish the voluntary finger extension from thumb adduction and finger flexion using stroke patients' electroenc...

The motor impaired people have much limit in moving. The devices augmenting their mobility will be much helpful for improving their living experiences. This poster develops a brain-controlled assistive system, called FlyingBuddy2, to aid the handicapped in mobility. It uses the brain EEG signals to directly control a quadrotor. Signals from an EEG...

Automatic seizure detection from the electroen-cephalogram (EEG) plays an important role in an on-demand closed-loop therapeutic system. A new feature, called IMF-VoE, is proposed to predict the occurrence of seizures. The IMF-VoE feature combines three intrinsic mode functions (IMFs) from the empirical mode decomposition of a EEG signal and the va...

This paper presents a hybrid brain-computer interface (BCI) control strategy, the goal of which is to expand control functions of a conventional motor imagery or a P300 potential based BCI in a virtual environment. The hybrid control strategy utilizes P300 potential to control virtual devices and motor imagery related sensorimotor rhythms to naviga...

A brain computer interface (BCI) builds an additional pathway between the brain and external devices. This paper introduces a BCI system combined with virtual reality (VR) technology that allows users to navigate in the virtual apartment by motor imagery. Since performance in online sessions is sometimes found to decrease due to non-stationarity of...