| 2025 | Abdallah et al. | A hybrid EMG–EEG interface for robust intention detection and fatigue-adaptive control of an elbow rehabilitation robot | five healthy participants (3 females, age 26–39) |
| 2018 | Bousseta et al. | EEG Based Brain Computer Interface for Controlling a Robot Arm Movement Through Thought | Four subjects (1 female and 3 males) aged between 20 and 29 years |
| 2023 | Catalan et al. | Hybrid brain/neural interface and autonomous vision-guided whole-arm exoskeleton control to perform activities of daily living (ADLs) | Ten impaired participants (5 males and 5 females, mean age 52 ± 16 years) |
| 2024 | Li et al. | An effective classification approach for EEG-based motor imagery tasks combined with attention mechanisms | - 109 subjects
- > 1, 500 recordings |
| 2024 | Choi et al. | On the Feasibility of EEG-based Motor Intention Detection for Real-Time Robot Assistive Control | Two subjects were mentioned. Total no. is unspecified |
| 2025 | Ding et al. | EEG-based brain-computer interface enables real-time robotic hand control at individual finger level | 21 able-bodied experienced BCI users |
| 2025 | Forenzo et al. | Continuous Reaching and Grasping With a BCI Controlled Robotic Arm in Healthy and Stroke-Affected Individuals | - 8 healthy subjects (average age: 26.125, 7 right-handed, 5 male)
- 5 with a history of stroke (average age: 57, 4 right-handed, 3 male) |
| 2025 | Kim et al. | Development of Multimodal EEG-EMG Human Machine Interface for Hand-Wrist Rehabilitation: A Preliminary Study | four healthy subjects |
| 2025 | Ghosh et al. | Hybrid brain-computer interfacing paradigm for assistive robotics | - 6 healthy, right handed male participants (average of 25 years old)
- 7 healthy, right handed female participants (average of 26 years old) |
| 2023 | Li et al. | A sequential learning model with GNN for EEG-EMG-based stroke rehabilitation BCI | - 5 healthy volunteers (516 trials)
- 2 stroke patients (174 trials) |
| 2022 | Muhammad et al. | Design and Development of Low-cost Wearable Electroencephalograms (EEG) Headset | 20 subjects |
| 2024 | Olikkal et al. | A Hybrid EEG-EMG Framework for Humanoid Control using Deep Learning Transformers | Ten able-bodies subjects |
| 2025 | Quesada et al. | EMG feature extraction and muscle selection for continuous upper limb movement regression | - 11 male participants
- 6 female participants
- age of participants: age: 28.2±7 |
| 2024 | Salah et al. | EEG-Based Brain-Computer Interface (BCI) Controlled Robotic Arm | 5 healthy participants (aged 22-24 years old) |
| 2025 | Wang et al. | Hybrid Brain-Machine Interface: Integrating EEG and EMG for Reduced Physical Demand | Twelve able-bodied participants (aged 18-21 years old) |
| 2025 | Wang et al. | EEG-Driven AR-Robot System for Zero-Touch Grasping Manipulation | - 2 male participants
- 1 female participant
- participants are all healthy, aged 22-24 |
| 2023 | Wang et al. | Development of a whole-body walking rehabilitation robot and power assistive method using EMG signals | 1 test subject (an author of the study) |
| 2024 | Zandigohar et al. | Multimodal fusion of EMG and vision for human grasp intent inference in prosthetic hand control | - 4 healthy male subjects
- 1 healthy female subject
- mean age of 26.7±2.5 years |
| 2024 | Zhang et al. | Research on shared control of robots based on hybrid brain-computer interface | - 6 male subjects
- 2 female subjects
- all healthy |
| 2022 | Xu et al. | Continuous Hybrid BCI Control for Robotic Arm Using Noninvasive Electroencephalogram, Computer Vision, and Eye Tracking | - Seven right-handed subjects (4 males 3 females)
- average age of 23.1±0.6 years
- No history of neurological diseases |
| 2022 | Yu et al. | Effects of Motor Imagery Tasks on Brain Functional Networks Based on EEG Mu/Beta Rhythm | - 16 healthy right-handed subjects (8 females, 8 males)
- ages between 20 to 25 |
| 2022 | Pawuś & Paszkiel | Application of EEG Signals Integration to Proprietary Classification Algorithms in the Implementation of Mobile Robot Control with the Use of Motor Imagery Supported by EMG Measurements | 10 participants from different ages, sex, and differed in other factors (unspecified in the study) |