Non-invasive brain-computer interfaces (BCIs) have the potential to enable intuitive control of prosthetic limbs for individuals with upper limb amputations. However, existing EEG-based control systems face challenges related to signal noise, classification accuracy, and real-time adaptability. In this work, we present BRAVE, a hybrid EEG and voice-controlled prosthetic system that integrates ensemble learning-based EEG classification with a human-in-the-loop (HITL) correction framework for enhanced responsiveness. Unlike traditional electromyography (EMG)-based prosthetic control, BRAVE aims to interpret EEG-driven motor intent, enabling movement control without reliance on residual muscle activity. To improve classification robustness, BRAVE combines LSTM, CNN, and Random Forest models in an ensemble framework, achieving a classification accuracy of 96% across test subjects. EEG signals are preprocessed using a bandpass filter (0.5-45 Hz), Independent Component Analysis (ICA) for artifact removal, and Common Spatial Pattern (CSP) feature extraction to minimize contamination from electromyographic (EMG) and electrooculographic (EOG) signals. Additionally, BRAVE incorporates automatic speech recognition (ASR) to facilitate intuitive mode switching between different degrees of freedom (DOF) in the prosthetic arm. The system operates in real time, with a response latency of 150 ms, leveraging Lab Streaming Layer (LSL) networking for synchronized data acquisition. The system is evaluated on an in-house fabricated prosthetic arm and on multiple participants highlighting the generalizability across users. The system is optimized for low-power embedded deployment, ensuring practical real-world application beyond high-performance computing environments. Our results indicate that BRAVE offers a promising step towards robust, real-time, non-invasive prosthetic control.

Efficient control of prosthetic limbs via non-invasive brain-computer interfaces (BCIs) requires advanced EEG processing, including pre-filtering, feature extraction, and action prediction, performed in real time on edge AI hardware. Achieving this on resource-constrained devices presents challenges in balancing model complexity, computational efficiency, and latency. We present CognitiveArm, an EEG-driven, brain-controlled prosthetic system implemented on embedded AI hardware, achieving real-time operation without compromising accuracy. The system integrates BrainFlow, an open-source library for EEG data acquisition and streaming, with optimized deep learning (DL) models for precise brain signal classification. Using evolutionary search, we identify Pareto-optimal DL configurations through hyperparameter tuning, optimizer analysis, and window selection, analyzed individually and in ensemble configurations. We apply model compression techniques such as pruning and quantization to optimize models for embedded deployment, balancing efficiency and accuracy. We collected an EEG dataset and designed an annotation pipeline enabling precise labeling of brain signals corresponding to specific intended actions, forming the basis for training our optimized DL models. CognitiveArm also supports voice commands for seamless mode switching, enabling control of the prosthetic arm's 3 degrees of freedom (DoF). Running entirely on embedded hardware, it ensures low latency and real-time responsiveness. A full-scale prototype, interfaced with the OpenBCI UltraCortex Mark IV EEG headset, achieved up to 90% accuracy in classifying three core actions (left, right, idle). Voice integration enables multiplexed, variable movement for everyday tasks (e.g., handshake, cup picking), enhancing real-world performance and demonstrating CognitiveArm's potential for advanced prosthetic control.

New prosthetic arms combine artificial intelligence, machine learning and advanced sensor systems. If you've ever wondered what's next for prosthetic technology, you're not alone. For many people living with limb loss, finding a prosthetic that feels natural and works seamlessly with their body has always been a challenge. Now, a California startup called Atom Bodies is making headlines for its groundbreaking approach to prosthetic technology. By combining artificial intelligence, machine learning and advanced sensor systems, Atom Bodies is developing mind-controlled robotic arms that could soon make highly advanced prosthetics accessible to thousands of amputees.

Upper-limb amputees face tremendous difficulty in operating dexterous powered prostheses. Previous work has shown that aspects of prosthetic hand, wrist, or elbow control can be improved through "intelligent" control, by combining movement-based or gaze-based intent estimation with low-level robotic autonomy. However, no such solutions exist for whole-arm control. Moreover, hardware platforms for advanced prosthetic control are expensive, and existing simulation platforms are not well-designed for integration with robotics software frameworks. We present the Prosthetic Arm Control Testbed (ProACT), a platform for evaluating intelligent control methods for prosthetic arms in an immersive (Augmented Reality) simulation setting. Using ProACT with non-amputee participants, we compare performance in a Box-and-Blocks Task using a virtual myoelectric prosthetic arm, with and without intent estimation. Our results show that methods using intent estimation improve both user satisfaction and the degree of success in the task. To the best of our knowledge, this constitutes the first study of semi-autonomous control for complex whole-arm prostheses, the first study including sequential task modeling in the context of wearable prosthetic arms, and the first testbed of its kind. Towards the goal of supporting future research in intelligent prosthetics, the system is built upon on existing open-source frameworks for robotics.

Currently, people with disability or difficulty to move their arms (referred to as "patients") have very limited technological solutions to efficiently address their physiological limitations. It is mainly due to two reasons: (1) the non-invasive solutions like mind-controlled prosthetic devices are typically very costly and require expensive maintenance; and (2) other solutions require costly invasive brain surgery, which is high risk to perform, expensive, and difficult to maintain. Therefore, current technological solutions are not accessible for all patients with different financial backgrounds. Toward this, we propose a low-cost technological solution called MindArm, a mechanized intelligent non-invasive neuro-driven prosthetic arm system. Our MindArm system employs a deep neural network (DNN) engine to translate brain signals into the intended prosthetic arm motion, thereby helping patients to perform many activities despite their physiological limitations. Here, our MindArm system utilizes widely accessible and low-cost surface electroencephalogram (EEG) electrodes coupled with an Open Brain Computer Interface and UDP networking for acquiring brain signals and transmitting them to the compute module for signal processing. In the compute module, we run a trained DNN model to interpret normalized micro-voltage of the brain signals, and then translate them into a prosthetic arm action via serial communication seamlessly. The experimental results on a fully working prototype demonstrate that, from the three defined actions, our MindArm system achieves positive success rates, i.e., 91\% for idle/stationary, 85\% for shake hand, and 84\% for pick-up cup. This demonstrates that our MindArm provides a novel approach for an alternate low-cost mind-controlled prosthetic devices for all patients.

A man who had his right arm amputated below the elbow has been able to feel hot and cold in his missing hand via a modified prosthetic arm with thermal sensors. After an amputation, some people can still perceive touch and pain sensations in their missing arm or leg, known as a phantom limb. Sometimes, these sensations can be triggered by nerve endings in the residual upper limb. The prosthetic works by applying heat or cold to the skin on the upper arm in specific locations that trigger a thermal sensation in the phantom hand. "In a previous study, we have shown the existence of these spots in the majority of amputee patients that we have treated," says Solaiman Shokur at the Swiss Federal Institute of Technology in Lausanne.

A seven-year-old boy born without a right hand is now beaming with joy as he tried out his new'robot arm'. Louie Morgan-Kemp, of Swavesey, Cambridgeshire, had just started fundraising for the prosthetic when a kind-hearted businessman saw his story in the news and offered to pay the full £13,000 cost. The youngster collected the Ironman-themed Hero Arm this week and can move its mechanical fingers by using muscles in his arm to press buttons inside the sleeve. Louie said the gadget, made by Bristol-based Open Bionics, helps him with picking things up, cutting food and pouring drinks. He said it was'exciting' to get the arm and he was'happy' that businessman Billy Dixon had paid for him to get it.

The thought of humans having wings, tentacles or an extra arm may all seem rather unlikely. But these scenarios could actually become reality in the next few decades, thanks to leaps in human augmentation. Researchers have already designed a'Third Thumb' controlled by foot movements, which allows the wearer to unscrew a bottle, peel a banana or thread a needle using just one hand. Now, experts believe the thumb is just a first step towards larger, more dramatic additions to the human body. Tamar Makin, a professor of cognitive neuroscience at Cambridge University, said the brain's ability to adapt to an extra limb was'extraordinary'.

I was born with Poland syndrome, a disease that prevented the formation of my right arm and pectoral muscles. I was bullied at school. People said things like, "It's not your fault that you were born like this, it's your mother's fault." Or asked me to catch a ball with my right hand. Stupid comments that wouldn't affect me now, but back then they struck very hard.

As Verne understood, the U.S. Civil War (during which 60,000 amputations were performed) inaugurated the modern prosthetics era in the United States, thanks to federal funding and a wave of design patents filed by entrepreneurial prosthetists. The two World Wars solidified the for-profit prosthetics industry in both the United States and Western Europe, and the ongoing War on Terror helped catapult it into a US $6 billion dollar industry across the globe. This recent investment is not, however, a result of a disproportionately large number of amputations in military conflict: Around 1,500 U.S. soldiers and 300 British soldiers lost limbs in Iraq and Afghanistan. Limb loss in the general population dwarfs those figures. A much smaller subset--between 1,500 to 4,500 children each year--are born with limb differences or absences, myself included.