TABLE 1.
Some interesting examples of related work for BCI applications for elderly people.
| Author | Participant | BCI paradigm | Target | Results |
| Aging and cognitive impairments | ||||
| Lee et al., 2013 | 31 healthy elderly (aged between 60–70 years old) | Using EEG for study the cognitive abilities: memory and attention | To improve the memory and attention of elderly patients with playing the card pairing memory game. The participants need to focus their attention for open and close the card on the screen | Significant improvement in immediate memory (p = 0.038) visuospatial/constructional (p = 0.014), attention (p = 0.039), and delayed memory (p < 0.001) scores |
| Gomez-Pilar et al., 2016 | 63 subjects (more than 60 years old) | 8 EEG electrodes for study the memory ability | Combines hand motor imagery tasks with memory exercise. The participants need to remember the repeated item | significant improvements (p < 0.01) in four cognitive functions after performing five (Neurofeedback Training sessions: visuospatial, oral language, memory, and intellectual |
| Foong et al., 2019 | 11 stroke patients (mean age 55.2 ± 11.0 years) | EEG (Motor Imagery) for study the assessment of the efficacy of EEG-based MI-BCI with visual feedback and EEG correlates of mental fatigue for upper-limb stroke rehabilitation | The participants need to imagine to move the upper arm (stroke-affected side) to reach the target in front of them | Have significant improvement from baseline until week 6 and 24 based on Fugl-meyer motor assessment (FMA). Also have significant positive correlations between frontal and central brain regions |
| Renton et al., 2019 | 17 naïve participants | EEG (SSVEP) for free communication between naïve human participants | The participants were facing two experiments: (i) to test either participant can maintain the rapid typing for free words, (ii) two participants will have free communication based on social BCI communication interface | Based on the naïve participants, the free communication is possible but the information transfer was reduced because of the textual correction during the free communication |
| Xiao et al., 2018 | 5 healthy participants (29 ± 5 years old) 15 patients of disorders of consciousness (13–73 years old) | EEG to study visual fixation assessment in patients with disorders of consciousness | Five healthy participants validated the proposed system. The DOC patients going thru two different visual fixation experiments; (i) Coma recovery scale-revised (CRS-R) based behavioral assessment, (ii) BCI based assessment | Three from the 15 patients showed visual fixation based on two experiments while one patient achieved significant online accuracy in BCI assessment. Thus, the BCI can be promising tools for assisting behavioral in CRS-R. |
| Aging and motor control impairments | ||||
| Herweg et al., 2016 | 10 healthy elderly (50–73 years old) | EEG (ERP) for controlling a virtual wheelchair | The participants need to reach three checkpoints using a virtual wheelchair with 14 movements commands | Average accuracy during the navigation tasks was above 90% and the optional task has an average of accuracy above 95% |
| Kaufmann et al., 2014 | 17 healthy participants (18–27 years old) | EEG (ERP) for controlling a wheelchair | The people with severe disabilities example neurodegenerative disease to control the wheelchair | 11 participants successfully complete the task of reaching four checkpoints in the building |
| Villa-Parra et al., 2015 | 4 healthy subjects | EEG and surface EMG (sEMG) for study motor control impairment to control a robotic knee exoskeleton | To improve mobility and security for gait rehabilitation. The activities are focusing on stand-up/sit-down and knee flexion/extension | A combination of EEG/sEMG can be used to identify the control strategy to develop a system to help and restore users with muscular disabilities. |
| Lee et al., 2017 | 5 subjects (20–35 years old) | 16 EEG electrodes for controlling an exoskeleton | The participants need to control three different directions: walk in front, turn left and turn right | All five subjects successfully complete the 3 ways navigation tasks and time decrease 10.2% from overall tasks from baseline protocol |
| Jafri et al., 2019 | 60 subjects (young < 50 and older than > 50) | EEG for controlling smart home and medical system | To design and evaluate the system for disabled and older adults to perform the daily task such as operating and control the home and medical appliances (a light bulb, a fan and digital blood pressure monitor) | The younger male reached attention level up to 74.78 with 26.2 s quicker than younger females and older people. The wireless BCI (WBCI) suitable for all people with brain and eyes are functional or the body of paralyzed |
| Chai et al., 2020 | 5 healthy people (mean age 23.8 ± 1.1 years) 5 paralyzed patients (mean age 48.8 ± 7.9 years) | EEG (SSVEP) with EMG to develop hBCI for smart home control | The participants need to control a wheelchair, nursing bed, curtain/light, television and telephones | The average accuracy is 97.5% for healthy participants and 83.6% for paralysis patients |