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We present a hybrid brain-machine interface (BMI) that integrates steady-state visually evoked potential (SSVEP)-based EEG and facial EMG to improve multimodal control and mitigate fatigue in assistive applications. Traditional BMIs relying…

Neurons and Cognition · Quantitative Biology 2025-02-18 Daniel Wang , Katie Hong , Zachary Sayyah , Malcolm Krolick , Emma Steinberg , Rohan Venkatdas , Sidharth Pavuluri , Yipeng Wang , Zihan Huang

Electroencephalography (EEG) is a non-invasive technique for recording brain electrical activity, widely used in brain-computer interface (BCI) and healthcare. Recent EEG foundation models trained on large-scale datasets have shown improved…

Machine Learning · Computer Science 2025-09-29 Yi Ding , Muyun Jiang , Weibang Jiang , Shuailei Zhang , Xinliang Zhou , Chenyu Liu , Shanglin Li , Yong Li , Cuntai Guan

Analyzing and reconstructing visual stimuli from brain signals effectively advances the understanding of human visual system. However, the EEG signals are complex and contain significant noise. This leads to substantial limitations in…

Computer Vision and Pattern Recognition · Computer Science 2025-04-24 Honghao Fu , Zhiqi Shen , Jing Jih Chin , Hao Wang

Conventional augmentative and alternative communication (AAC) systems and language-learning platforms often fail to adapt in real time to the user's cognitive and linguistic needs, especially in neurological conditions such as post-stroke…

Human-Computer Interaction · Computer Science 2025-08-01 Ismail Hossain , Mridul Banik

The Electromyography (EMG) signal is the electrical activity produced by cells of skeletal muscles in order to provide a movement. The non-invasive prosthetic hand works with several electrodes, placed on the stump of an amputee, that…

Machine Learning · Computer Science 2016-05-25 Valentina Gregori

The "mind-controlling" capability has always been in mankind's fantasy. With the recent advancements of electroencephalograph (EEG) techniques, brain-computer interface (BCI) researchers have explored various solutions to allow individuals…

Human-Computer Interaction · Computer Science 2021-04-15 Di Wu , Huayan Wan , Siping Liu , Weiren Yu , Zhanpeng Jin , Dakuo Wang

One of the greatest goals of neuroscience in recent decades has been to rehabilitate individuals who no longer have a functional relationship between their mind and their body. Although neuroscience has produced technologies which allow the…

Human-Computer Interaction · Computer Science 2021-07-02 Samuel Kuhn , Nathan George

The non-stationary nature of electroencephalography (EEG) signals makes an EEG-based brain-computer interface (BCI) a dynamic system, thus improving its performance is a challenging task. In addition, it is well-known that due to…

Machine Learning · Computer Science 2018-05-04 Haider Raza , Dheeraj Rathee , ShangMing Zhou , Hubert Cecotti , Girijesh Prasad

This paper presents a novel neuromorphic control architecture for upper-limb prostheses that combines surface electromyography (sEMG) with gaze-guided computer vision. The system uses a spiking neural network deployed on the neuromorphic…

Building robotic prostheses requires a sensor-based interface designed to provide the robotic hand with the control required to perform hand gestures. Traditional Electromyography (EMG) based prosthetics and emerging alternatives often face…

Robotics · Computer Science 2025-07-15 Dean Zadok , Oren Salzman , Alon Wolf , Alex M. Bronstein

Brain-robot interaction (BRI) empowers individuals to control (semi-)automated machines through their brain activity, either passively or actively. In the past decade, BRI systems have achieved remarkable success, predominantly harnessing…

Objective: Spinal cord injury (SCI) often leaves affected individuals unable to ambulate. Electroencephalogramme (EEG) based brain-computer interface (BCI) controlled lower extremity prostheses may restore intuitive and able-body-like…

Human-Computer Interaction · Computer Science 2012-08-31 Po T. Wang , Christine E. King , Luis A. Chui , An H. Do , Zoran Nenadic

Restoring limb motor function in individuals with spinal cord injury (SCI), stroke, or amputation remains a critical challenge, one which affects millions worldwide. Recent studies show through surface electromyography (EMG) that spared…

A brain--machine interface (BMI) based on motor imagery (MI) enables the control of devices using brain signals while the subject imagines performing a movement. It plays a vital role in prosthesis control and motor rehabilitation. To…

Signal Processing · Electrical Eng. & Systems 2024-09-20 Xiaying Wang , Michael Hersche , Michele Magno , Luca Benini

In the context of a Brain Computer Interface platform implemented for the arm rehabilitation of mildly impaired stroke patients, two methods of EEG signals processing are compared in terms of (i) their identification performance rate and…

Signal Processing · Electrical Eng. & Systems 2020-09-01 Giulia Cisotto , Silvano Pupolin , Francesco Piccione

Brain computer interface (BCI) provides promising applications in neuroprosthesis and neurorehabilitation by controlling computers and robotic devices based on the patient's intentions. Here, we have developed a novel BCI platform that…

Robotics · Computer Science 2017-07-25 Reza Abiri , Griffin Heise , Xiaopeng Zhao , Yang Jiang , Fateme Abiri

The integration of brain-computer interfaces (BCIs) into the realm of smart wheelchair (SW) technology signifies a notable leap forward in enhancing the mobility and autonomy of individuals with physical disabilities. BCIs are a technology…

Human-Computer Interaction · Computer Science 2024-04-30 Shiva Ghasemi , Denis Gracanin , Mohammad Azab

The identification of intentionally delivered commands is a challenge in Brain Computer Interfaces (BCIs) based on Sensory-Motor Rhythms (SMR). It is of fundamental importance that BCI systems controlling a robotic device (i.e., upper limb…

Human-Computer Interaction · Computer Science 2019-05-27 Tortora Stefano , Beraldo Gloria , Tonin Luca , Menegatti Emanuele

Objective: For transradial amputees, robotic prosthetic hands promise to regain the capability to perform daily living activities. Current control methods based on physiological signals such as electromyography (EMG) are prone to yielding…

Electrocardiogram (EMG) signals play a significant role in decoding muscle contraction information for robotic hand prosthesis controllers. Widely applied decoders require large amount of EMG signals sensors, resulting in complicated…

Optimization and Control · Mathematics 2022-11-30 Jin Ren , Guohui Song , Lucia Tabacu , Yuesheng Xu