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The Two-Brains Hypothesis: Towards a guide for brain-brain and brain-machine interfaces

Goodman G, Poznanski RR, Cacha L, Bercovich D

J Integr Neurosci, 2015 Sep;14(3):281-93.
PMID: 26477360 DOI: 10.1142/S0219635215500235

Great advances have been made in signaling information on brain activity in individuals, or passing between an individual and a computer or robot. These include recording of natural activity using implants under the scalp or by external means or the reverse feeding of such data into the brain. In one recent example, noninvasive transcranial magnetic stimulation (TMS) allowed feeding of digitalized information into the central nervous system (CNS). Thus, noninvasive electroencephalography (EEG) recordings of motor signals at the scalp, representing specific motor intention of hand moving in individual humans, were fed as repetitive transcranial magnetic stimulation (rTMS) at a maximum intensity of 2.0[Formula: see text]T through a circular magnetic coil placed flush on each of the heads of subjects present at a different location. The TMS was said to induce an electric current influencing axons of the motor cortex causing the intended hand movement: the first example of the transfer of motor intention and its expression, between the brains of two remote humans. However, to date the mechanisms involved, not least that relating to the participation of magnetic induction, remain unclear. In general, in animal biology, magnetic fields are usually the poor relation of neuronal current: generally "unseen" and if apparent, disregarded or just given a nod. Niels Bohr searched for a biological parallel to complementary phenomena of physics. Pertinently, the two-brains hypothesis (TBH) proposed recently that advanced animals, especially man, have two brains i.e., the animal CNS evolved as two fundamentally different though interdependent, complementary organs: one electro-ionic (tangible, known and accessible), and the other, electromagnetic (intangible and difficult to access) - a stable, structured and functional 3D compendium of variously induced interacting electro-magnetic (EM) fields. Research on the CNS in health and disease progresses including that on brain-brain, brain-computer and brain-robot engineering. As they grow even closer, these disciplines involve their own unique complexities, including direction by the laws of inductive physics. So the novel TBH hypothesis has wide fundamental implications, including those related to TMS. These require rethinking and renewed research engaging the fully complementary equivalence of mutual magnetic and electric field induction in the CNS and, within this context, a new mathematics of the brain to decipher higher cognitive operations not possible with current brain-brain and brain-machine interfaces. Bohr may now rest.

Matched MeSH terms: Robotics
Fulltext Paediatric transoral robotic-assisted adenotonsillectomy: a new anaesthetic experience in Malaysia.

Zawiah Kassim, Fauziah Ahmad, Rusnaini Mustapha Kamar, Karis Misiran

Journal of Clinical and Health Sciences, 2019;4(2):91-96.
MyJurnal

Safety and feasibility of transoral robotic surgery (TORS) in adults for otolaryngology surgery,
mainly in the treatment of oropharyngeal carcinoma and obstructive sleep apnoea has already
been established several years ago. However, less is known with respect to the role and safety
of TORS for otolaryngology surgery in the paediatric age group and its description in the
literature is currently insufficient. As paediatric patients are unique in their anatomy, physiology
and pharmacological kinetic, special attention and consideration has to be applied when using
TORS, hence this increases the perioperative challenges. Herewith we present our experience
in anaesthetising a paediatric patient for TORS adenotonsillectomy which is the first not only
in our centre but in Malaysia. Our major obstacle was the limited airway access as the area of
concern was shared by the anaesthesiologist, surgeon and also the robotic system.
Haemodynamic stabilisation was a challenge compared to the conventional method as the
operative time increased due to robot docking time and the new surgical learning process. In
our opinion, the key point for the success of TORS adenotonsillectomy in paediatric patients is
good communication and teamwork between all personnel involved in the surgery.

Matched MeSH terms: Robotics
Fulltext The classification of movement intention through machine learning models: the identification of significant time-domain EMG features

Mohd Khairuddin I, Sidek SN, P P Abdul Majeed A, Mohd Razman MA, Ahmad Puzi A, Md Yusof H

PeerJ Comput Sci, 2021;7:e379.
PMID: 33817026 DOI: 10.7717/peerj-cs.379

Electromyography (EMG) signal is one of the extensively utilised biological signals for predicting human motor intention, which is an essential element in human-robot collaboration platforms. Studies on motion intention prediction from EMG signals have often been concentrated on either classification and regression models of muscle activity. In this study, we leverage the information from the EMG signals, to detect the subject's intentions in generating motion commands for a robot-assisted upper limb rehabilitation platform. The EMG signals are recorded from ten healthy subjects' biceps muscle, and the movements of the upper limb evaluated are voluntary elbow flexion and extension along the sagittal plane. The signals are filtered through a fifth-order Butterworth filter. A number of features were extracted from the filtered signals namely waveform length (WL), mean absolute value (MAV), root mean square (RMS), standard deviation (SD), minimum (MIN) and maximum (MAX). Several different classifiers viz. Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN) were investigated on its efficacy to accurately classify the pre-intention and intention classes based on the significant features identified (MIN and MAX) via Extremely Randomised Tree feature selection technique. It was observed from the present investigation that the DT classifier yielded an excellent classification with a classification accuracy of 100%, 99% and 99% on training, testing and validation dataset, respectively based on the identified features. The findings of the present investigation are non-trivial towards facilitating the rehabilitation phase of patients based on their actual capability and hence, would eventually yield a more active participation from them.

Matched MeSH terms: Robotics
Fulltext A practical model for endodontic radiography teaching

Che Ab Aziz, Z.A.

Ann Dent, 2008;15(2):67-70.
MyJurnal

Aim: To manufacture a clinical simulation apparatus for the undergraduates' endodontic radiography teaching Objectives: • To provide a model for teaching of parallax method using Kelly's forcep • To provide a model for undergraduates to practice radiographic localization employing parallax method. • To allow students to practice taking radiographs in a way that simulates the clinical situations with a good diagnostic quality Methods: Impressions of a dentate arch (maxillary and mandibullary) were used to form a stone cast. A section of the cast, in the area where the natural teeth were to be placed, is sectioned and removed. Three maxillary extracted teeth (canine, first and second premolar) were selected and mounted with acrylic resin at the sectioned area. The resin was cured in a light box. The arches were mounted in a phantom head with a placement of rubber cheek. The first premolar was isolated with rubber dam. The intraoral holder (Kelly's forcep) was attached to a robotic arm. The students were taught the correct angulations of the x-ray cone for the paralleling technique and parallax method using Kelly's forcep during root canal treatment. Results: All students managed to complete the exercise and were considered competent when they produced acceptable quality of radiographs. Conclusion: The model described was improvised from a model that has been used during the past 2 years for undergraduates' endodontic courses. It has been well accepted as it simulates the clinical situation more closely than was possible previously.

Matched MeSH terms: Robotics
A review of disability EEG based wheelchair control system: Coherent taxonomy, open challenges and recommendations

Al-Qaysi ZT, Zaidan BB, Zaidan AA, Suzani MS

Comput Methods Programs Biomed, 2018 Oct;164:221-237.
PMID: 29958722 DOI: 10.1016/j.cmpb.2018.06.012

CONTEXT: Intelligent wheelchair technology has recently been utilised to address several mobility problems. Techniques based on brain-computer interface (BCI) are currently used to develop electric wheelchairs. Using human brain control in wheelchairs for people with disability has elicited widespread attention due to its flexibility.
OBJECTIVE: This study aims to determine the background of recent studies on wheelchair control based on BCI for disability and map the literature survey into a coherent taxonomy. The study intends to identify the most important aspects in this emerging field as an impetus for using BCI for disability in electric-powered wheelchair (EPW) control, which remains a challenge. The study also attempts to provide recommendations for solving other existing limitations and challenges.
METHODS: We systematically searched all articles about EPW control based on BCI for disability in three popular databases: ScienceDirect, IEEE and Web of Science. These databases contain numerous articles that considerably influenced this field and cover most of the relevant theoretical and technical issues.
RESULTS: We selected 100 articles on the basis of our inclusion and exclusion criteria. A large set of articles (55) discussed on developing real-time wheelchair control systems based on BCI for disability signals. Another set of articles (25) focused on analysing BCI for disability signals for wheelchair control. The third set of articles (14) considered the simulation of wheelchair control based on BCI for disability signals. Four articles designed a framework for wheelchair control based on BCI for disability signals. Finally, one article reviewed concerns regarding wheelchair control based on BCI for disability signals.
DISCUSSION: Since 2007, researchers have pursued the possibility of using BCI for disability in EPW control through different approaches. Regardless of type, articles have focused on addressing limitations that impede the full efficiency of BCI for disability and recommended solutions for these limitations.
CONCLUSIONS: Studies on wheelchair control based on BCI for disability considerably influence society due to the large number of people with disability. Therefore, we aim to provide researchers and developers with a clear understanding of this platform and highlight the challenges and gaps in the current and future studies.

Matched MeSH terms: Robotics