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(a) GUI Prosthetic Hand on the Front Panel; (b) GUI Prosthetic Hand Flow Signal on the Block Diagram.
Source publication
Prosthetic hand acts as a tool that enables the amputee to perform daily tasks. Instead of passive devices which are aesthetically pleasing, current devices come with improved functionality utilizing robotic technology. There are various ways to control a prosthetic hand. One of it includes Brain Computer Interface (BCI) which has advanced technolo...
Contexts in source publication
Context 1
... software and hardware programs. Time taken by users and channels involved will affect the time required for processing the data. Because of these problems, GUI must be more stable and efficient for controlling the signal obtained from the user's brain. A simple GUI template was done by using two consumers' setting and simulation as shown in Fig. 3 (a). The first consumer's setting is used to determine the connection setting USB COM Port and bit data. For the second consumer, interface integration serves as a data graph that shows the eye movements of the subject. There is also the status of eye movement and status of subject movement of the prosthetic hand. It also shows an error ...
Context 2
... Emotiv Start Task palette is used to connect an edf file data generated by Emotiv Testbench that directly connects to the new interface. Besides those two mentioned previously, there are other palettes like Emotiv Read (to detect facial expressions or smiles while looking right), Status Graph, VISA, Emotiv Stop Task and error status as shown in Fig. 3 ...
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The usual body-powered prosthetics is tiring and lead to compliance and restoration problems. Brain computer interface (BCI) prosthetic is one of the advanced technologies opening up new possibility in providing healthcare solutions for people with severe motor impairment. Generally, electroencephalography (EEG) is historically dominated by BCI res...
Citations
... Hand gesture can be categorized into static hand gestures and dynamic hand gestures [12]. Controlled prosthetic hand are used by trans-radial amputee and may likewise discover applications for the elderly and frail individuals [13]. ...
Brain computer interface (BCI) system empowers command over external device by retrieving brain waves and interpreting them into machine instructions. The system utilizes electroencephalogram (EEG) for receiving, processing and classifying signals to control by means of brain generated signals. The paper focuses on mental task design for BCI by acquiring the signals generated by mental activity through EEG comb electrodes, placed over three-dimensional (3D) printed headset. The experiment involved the blinking of left and right eye for the forward and backward movements of the prototype wheelchair. The experimental measurement was performed using a Cyton board where the information was transmitted through Bluetooth which were later processed and translated to the wheelchair to perform activities. The system has successfully achieved the real time control of an assistive device by using signals from the brain.
... Body-powered devices employ the use of a cabling and strap system tethered to the device which opens and closes the hand or hook via user body movements [6]. In contrast, externally powered devices require motors and batteries relying on either surface electrodes sensing muscle contractions, electroencephalogram (EEG) or brain-computer interface BCI technologies [7][8][9][10]. ...
Background
A body-powered functional and cosmetically appeasing hand with moving interphalangeal joints (IPJ-Hand) was created. Function and satisfaction with the IPJ-Hand, conventional hand (CH) and functional hook (FH) in trans-radial prosthesis users were evaluated.
Methods
Eight participants with trans-radial amputations were provided with three prosthetic hands and performed the Nine Hole Peg Test (NHPT), Brief Activity Measure of Upper Limb Amputees (BAM-ULA) and Quebec User Evaluation of Satisfaction with Assistive Technology QUEST).
Results
The data are shown as the median and interquartile range (IQR) due to skewed data distribution. Differences across hands were seen in NHPT with CH 57 (13.3), FH 49.5 (26.5), IPJ-H and 49 (14.8) seconds respectively. BAM-ULA, 10 (1.5), FH 10 (0.7), and IPJ-Hand 10 (1.7). QUEST scores, FH 28.5 (7.2) with the highest score, IPJ-Hand 26 (6.8), and lastly CH 25.5 (9.2).
Conclusion
Individual participant results varied, with some participants performing better on the NHPT when using the IPJ-Hand when compared to the CH and FH. No differences between hands on the BAM-ULA were seen, and although no large differences in QUEST were observed, users performed best using IPJ-Hand.
Key messages
An interphalangeal joint prosthetic hand (IPJ-Hand) offers the similar function of a prosthetic hook, and the appearance of a conventional hand, but with improved dexterity.
Minimal resources are needed to create the IPJ-Hand for prosthesis wearers.
... Thus, BCI provides a solution for patients with atretic states of almost complete paralysis, such as ALS and Parkinson's disease, as well as those who have suffered severe trauma due to stroke, cerebral palsy, or spinal cord or brain damage. [3] Much research on BCI applications has focused on rehabilitating the disabled, comprising brain-controlled wheelchairs [4][5][6], neuroprostheses [7][8][9], and communications equipment [10,11]. These BCI applications can improve the patient's quality of life while also reducing the expended energy and cost of family members and caregivers. ...
Brain-computer interfaces (BCIs), as a promising technology for rehabilitation, have attracted significant interest in the fields such as neurosciences, computer science, and biomedical engineering. Based on the expanded database of Science Citation Index, this paper analyzes bibliometrics in the field of BCI from 1990 to 2020 and discusses its potential research trends and prospects. The work provides a detailed overview of the BCI research status founded on the country, research area, institution, journal, author, citation, and keyword. A total of 7,880 papers suggest that the United States is leading the field of BCI research, followed by China and Germany. University of California system has produced the most publications, while the Graz University of Technology is leading the list for the h-index. Journal of Neural Engineering and IEEE Transactions on Neural Systems and Rehabilitation Engineering were identified to be the most productive journals in BCIs. Keywords analysis reflects that most research has focused on electroencephalography (EEG)-based BCIs to achieve a safe, real-time, stable link between the patient and artificial actuators. We can conclude by analyzing hot articles that new materials applied to neural probes may become the next hot topic. The typical paradigm of the BCI industry is the most urgent problem that can be solved in the standardization process. The rehabilitation applications appear as the initial driving force and ultimate goal for BCIs. This research can offer valuable references to BCI practitioners and provide data support for related studies.
... Therefore, the BCI applications [1][2] developed in the last 5 years might be obsolete due to the employment of Cortex V1 functions available at that time. Likewise, previous LabVIEW-based BCI applications [3] are not anymore usable at the current moment (the 2022 year) considering the unavailability of calling the Emotiv executable library. Nowadays, the Emotiv technology involves the EEG signal acquisition from Server and accessing the dedicated features by using WebSockets communication protocol. ...
This paper aims to design, explore and experiment with a new application of the brain-computer interface research field. By implementing a Python-based programming solution to acquire and analyze WebSocket's based EEG data from the portable Emotiv Insight headset, this scientific work highlights the opportunity to count the multiple eye blinks and use them for controlling an intelligent Arduino-based house prototype. Also, the wireless communication between the Python application and the Arduino program is enabled by WebSocket's protocol. The proposed brain-computer interface could improve the modalities of interaction with the surroundings as well as increase the independence and sense of usefulness for people with neuromotor disabilities. The encountered challenges focused mainly on handling the programming issues related to firstly synchronizing all the levels (Emotiv server, Python, and Arduino) and secondly avoiding the debouncing effect when counting the eye-blinks. Moreover, there were emphasized the advantages and disadvantages of creating a brain-computer interface by involving the hardware (EEG headsets) and software functionalities offered by Emotiv company.
... Accordingly, many studies have proposed various noninvasive methods for controlling prosthetic hands, such as electroencephalography-based brain-computer interface [2] and flex sensors attached to fingers [3]. These methods can allow the prosthetic or extra robotic hand to trigger opening and closing while not enabling many possible closing patterns of the multiple active DoF hand or are not suitable for transradial amputees. ...
Currently, prosthetic hands can only achieve several prespecified and discrete hand motion patterns from popular myoelectric control schemes using electromyography (EMG) signals. To achieve continuous and stable grasping within the discrete motion pattern, this paper proposes a control strategy using a customized, flexible capacitance-based proximity-tactile sensor. This sensor is integrated at the fingertip and measures the distance and force before and after contact with an object. During the pregrasping phase, each fingertip’s position is controlled based on the distance between the fingertip and the object to make all fingertips synchronously approach the object at the same distance. Once contact is established, the sensor turns to output the tactile information, by which the contact force of each fingertip is finely controlled. Finally, the method is introduced into the human-machine interaction control for a myoelectric prosthetic hand. The experimental results demonstrate that continuous and stable grasping could be achieved by the proposed control method within the subject’s discrete EMG motion mode. The subject also obtained tactile feedback through the transcutaneous electrical nerve stimulation after contact.
... An Arduino Uno was also used for the control of a prosthetic hand by an Emotiv EEG headset, as reported by Abu Kasim et al. [125]. A GUI was again based on LabVIEW, and LabVIEW and Arduino were connected by VISA virtual instruments. ...
To measure biosignals constantly, using textile-integrated or even textile-based electrodes and miniaturized electronics, is ideal to provide maximum comfort for patients or athletes during monitoring. While in former times, this was usually solved by integrating specialized electronics into garments, either connected to a handheld computer or including a wireless data transfer option, nowadays increasingly smaller single circuit boards are available, e.g., single-board computers such as Raspberry Pi or microcontrollers such as Arduino, in various shapes and dimensions. This review gives an overview of studies found in the recent scientific literature, reporting measurements of biosignals such as ECG, EMG, sweat and other health-related parameters by single circuit boards, showing new possibilities offered by Arduino, Raspberry Pi etc. in the mobile long-term acquisition of biosignals. The review concentrates on the electronics, not on textile electrodes about which several review papers are available.
... Clear examples are subjects affected by neuromuscular disorders, such as MD, ALS, MS, SCI, and CP, or even poststroke patients and amputees [5,[18][19][20]. In this scenario, two main targets can be identified: robotic control [14,15,34,49,50,[64][65][66][67][68][69] and prosthetic control [46][47][48][69][70][71][72][73][74][75][76][77][78]. ...
... Table 2 shows a summary of the selected EEG-based HMIs. Zhan Hong et al. [70] Scalp EEG Prosthetic Control Assistance Ortiz et al. [71] Scalp EEG Robotic Control Assistance, Rehabilitation Kasim et al. [72] Scalp EEG Prosthetic Control Assistance Murphy et al. [73] Scalp EEG Prosthetic Control Assistance Li et al. [74] sEEG Prosthetic Control Assistance Bhagat et al. [75] Scalp EEG Robotic Control Rehabilitation Morishita et al. [76] ECoG Prosthetic Control Rehabilitation Zhang et al. [77] Scalp EEG Prosthetic Control Assistance, Rehabilitation Yanagisawa et al. [78] ECoG Prosthetic Control Assistance, Rehabilitation He et al. [35] Scalp EEG Robotic Control Rehabilitation Tang et al. [79] Scalp EEG Robotic Control Assistance Randazzo et al. [80] Scalp EEG Robotic Control Assistance, Rehabilitation Li et al. [81] Scalp EEG Robotic Control Assistance, Rehabilitation Araujo et al. [83] Scalp EEG Robotic Control Rehabilitation Kashihara et al. [84] Scalp EEG Communication Assistance Mahmoudi and Erfanian [85] Scalp EEG Prosthetic Control Assistance ...
As a definition, Human–Machine Interface (HMI) enables a person to interact with a de‐ vice. Starting from elementary equipment, the recent development of novel techniques and unob‐ trusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal‐based HMIs for assistance and rehabilitation to outline state‐of‐the‐art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full‐text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever‐growing number of publica‐ tions has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate in‐ crease can be observed in studies focusing on robotic control, prosthetic control, and gesture recog‐ nition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs’ complex‐ ity, so their usefulness should be carefully evaluated for the specific application.
... The applications specific to medical and clinical treatment need approval. Kasim et al. [281] mentioned the dry electrode exhibited a greater resistance to line noise. Using several different sizes and shapes (cap) for head size variability experiments and studies can increase the overall price because of the need to buy more than one cap. ...
Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain–computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive.
... In addition, several researchers used different techniques to analyse the signals captured from human brains. For example, the work reported in [51] used commercial software to develop a GUI to train the human commands and translate them into a set of control signals to the robot. Other examples are the works presented in [52,53] which utilise genetic algorithm techniques to analyse brainwaves and control a robot accordingly. ...
This chapter reports a framework that can facilitate the interactions between a human’s EEG (electroencephalography) signals and an industrial robot. This can be achieved by using an EEG headset that captures the brain signals of the human and send it via Bluetooth to a local workstation for signal processing, feature extraction and classification. The system developed provides the ability for a shop-floor operator to control the robot using own brain signals. The system can cooperate with other channels of communications (gesture, voice, etc.) to strengthen the collaboration between the human and the robot during shared assembly operations. Such a collaboration aims to fuse the high accuracy of the robot with the high versatility of the human. Therefore, the aim is to exploit the strength of both sides and enhance the quality and adaptability of human–robot collaborative assembly operations. This approach is applicable to other types of robots as well, for example ones used for assisting people with severe disability.
... BCI is a field of research that is developed to collect human brainwaves and relate patterns in brain signals to the users' thoughts and intentions via electroencephalography (EEG). BCI is also known as Brain Machine Interface (BMI) or sometimes known as Direct Neural Interface [6]. BCI appears in many areas including control of various software applications (including video games, web browsers or typewriters), modern smart home applications or environment control, but mainly targets disabled people (wheelchairs, neural prosthetics, robotic arms) [7][8][9][10][11]. ...
