Signal Generation, Acquisition, and Processing in Brain Machine Interfaces: A Unified Review

Salahuddin, Usman; Gao, Pu‐Xian

doi:10.3389/fnins.2021.728178

Cited by 14 publications

(10 citation statements)

References 168 publications

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“…The device deciphers brain signals in five consecutive stages, namely, signal acquisition, pre-processing, feature extraction, classification, and control interface. 193 According to the depth of the embedded parts, BCI can be divided into non-invasive and invasive. Among them, non-invasive BCI which has the main disadvantages of low spatial resolution, low SNR, and signal distortion 194 exhibits better spatial and temporal resolutions than the non-invasive BCI.…”

Section: Recording Function Of Neural Signalsmentioning

confidence: 99%

Implantable neural electrodes: from preparation optimization to application

et al. 2023

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Section: Recording Function Of Neural Signalsmentioning

confidence: 99%

Implantable neural electrodes: from preparation optimization to application

et al. 2023

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“…Brain-machine interfaces (BMIs) have sometimes been proposed as the ultimate solution for voluntary motor decoding (Lebedev and Nicolelis, 2017 ; Salahuddin and Gao, 2021 ). Despite their great potential, BMIs should first overcome many non-trivial challenges before their wider adoption, including the improvement of surgery safety (Loeb and Richmond, 2021 ), the long-term stability of the electrodes (Chestek et al, 2011 ), decoding (Hill et al, 2012 ; Tankus et al, 2014 ; Jin et al, 2019 ), among others (Freire et al, 2011 ; Rapeaux and Constandinou, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Human-machine interface for two-dimensional steering control with the auricular muscles

et al. 2023

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Human-machine interfaces (HMIs) can be used to decode a user's motor intention to control an external device. People that suffer from motor disabilities, such as spinal cord injury, can benefit from the uses of these interfaces. While many solutions can be found in this direction, there is still room for improvement both from a decoding, hardware, and subject-motor learning perspective. Here we show, in a series of experiments with non-disabled participants, a novel decoding and training paradigm allowing naïve participants to use their auricular muscles (AM) to control two degrees of freedom with a virtual cursor. AMs are particularly interesting because they are vestigial muscles and are often preserved after neurological diseases. Our method relies on the use of surface electromyographic records and the use of contraction levels of both AMs to modulate the velocity and direction of a cursor in a two-dimensional paradigm. We used a locking mechanism to fix the current position of each axis separately to enable the user to stop the cursor at a certain location. A five-session training procedure (20–30 min per session) with a 2D center-out task was performed by five volunteers. All participants increased their success rate (Initial: 52.78 ± 5.56%; Final: 72.22 ± 6.67%; median ± median absolute deviation) and their trajectory performances throughout the training. We implemented a dual task with visual distractors to assess the mental challenge of controlling while executing another task; our results suggest that the participants could perform the task in cognitively demanding conditions (success rate of 66.67 ± 5.56%). Finally, using the Nasa Task Load Index questionnaire, we found that participants reported lower mental demand and effort in the last two sessions. To summarize, all subjects could learn to control the movement of a cursor with two degrees of freedom using their AM, with a low impact on the cognitive load. Our study is a first step in developing AM-based decoders for HMIs for people with motor disabilities, such as spinal cord injury.

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“…Patients who have been paralyzed after a stroke and those undergoing inpatient rehabilitation are aided by BCI technology in the medical field. The nonmedical applications are used in the internment applications such as video games [ 1 , 2 ]. Brain-machine interfaces (BMIs) use electric signals from the brain to control electronic devices without using limbs.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology

Albahri

Al-qaysi

Alzubaidi

et al. 2023

International Journal of Telemedicine and Applications

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The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered to gather relevant scientific and theoretical articles. Initially, 125 papers were found between 2010 and 2021 related to this integrated research field. After the filtering process, only 30 articles were identified and classified into five categories based on their type of deep learning methods. The first category, convolutional neural network (CNN), accounts for 70% ( n = 21 / 30 ). The second category, recurrent neural network (RNN), accounts for 10% ( n = 3 / 30 ). The third and fourth categories, deep neural network (DNN) and long short-term memory (LSTM), account for 6% ( n = 30 ). The fifth category, restricted Boltzmann machine (RBM), accounts for 3% ( n = 1 / 30 ). The literature’s findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. A comprehensive mapping analysis was also conducted, which identified six categories. Current challenges of ensuring trustworthy deep learning in SSVEP-based BCI applications were discussed, and recommendations were provided to researchers and developers. The study critically reviews the current unsolved issues of SSVEP-based BCI applications in terms of development challenges based on deep learning techniques and selection challenges based on multicriteria decision-making (MCDM). A trust proposal solution is presented with three methodology phases for evaluating and benchmarking SSVEP-based BCI applications using fuzzy decision-making techniques. Valuable insights and recommendations for researchers and developers in the SSVEP-based BCI and deep learning are provided.

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Signal Generation, Acquisition, and Processing in Brain Machine Interfaces: A Unified Review

Cited by 14 publications

References 168 publications

Implantable neural electrodes: from preparation optimization to application

Implantable neural electrodes: from preparation optimization to application

Human-machine interface for two-dimensional steering control with the auricular muscles

A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology

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