Table 1.
Research on EEG applications.
| Ref. | Prupose | Acquisition Method | Data Processing |
|---|---|---|---|
| Ma [42] | Recognize driver fatigue | Commercial Neuroscan system with 40 electrodes | Third-order Butterworth bandpass filter |
| Gamage [43] | Detect driver’s EEG to reduce traffic accidents | Evoke the emotions of the test driver with video and audio | EEGLAB Toolbox of Matlab |
| Shen [44] | Strengthen the depression recognition performance | Traditional 128-electrode mounted elastic cap and a wearable 3-electrode EEG collector | EEGLAB Toolbox of Matlab |
| Saedi [45] | Detect the working status of construction workers | Investigate mental and motor work | A mix of macro and micro scrutiny |
| Han [46] | Classification of eye state | EEG measured around the ear | Estimating classification accuracy using 3 CNN models |
| Pawuś and Paszkiel [47] | Use BCI to control the robot | Emotiv EPOC | Classic algorithms and the neural network |
| Chen [48] | EEG decoding | Obtained in the open world | Supervised deep learning |
| Pei [49] | PreG electrode in BCI | Obtained form PreG electrode | SSVEP-based BCI |
| Jemal [50] | Epileptic seizure prediction | Publicly available CHB-MIT dataset | Deep neural network model |
| Wen [51] | Evaluate spatial cognitive ability | From 7 subjects participating in the game | Coupling strength calculation |
| Li [52] | Emotion recognition | SJTU Emotion EEG Dataset | Experiment-level BN |
| Freismuth et al. [53] | Treatment and diagnosis of ADHD | Wearable EEG device | HiLCPS framework |
CNN: Convolutional neural network; BCI: Brain–machine interface; BN: Batch normalization; ADHD: Attention deficit hyperactivity disorder; HiLCPS: Human-in-the-loop cyber-physical systems.