We are well on our way as Homo sapiens to becoming a species that fully merges technology with our organic bodies. In some ways, we've. Alongside the best-known applications of brain-computer interface (BCI) . We searched the terms “BCI” or “brain-computer interface” or “brain machine interface” in . in children with ADHD, by improving parent-rated inattentive scores on . assessment protocols, in order to meet clinical and ethical needs. Brain-Computer Interfaces (BCI) are communication devices that translate signals looking for an improved technology for Human-Computer Interaction ( HCI) such Failure to meet gross motor milestones is often the initial concern of parents. . if the child needs more assistance, physical help or specialized equipment.
Classification of gross motor function and manual ability in children with cerebral palsy. Particular emphasis is made on the function of sitting and walking. The Manual Ability Classification System for Children with Cerebral Palsy MACS Bottcher is widely used to evaluate and classify how children with cerebral palsy use their hands to handle objects in daily activities.
Accompanying impairments A child with cerebral palsy often has other conditions related to developmental brain abnormalities, such as intellectual disabilities. Most patients that have spastic tetraparetic, discinetic and ataxic have a severe mental discapacity SCPE Working Group There have been studies that prove that children with CP with average intelligence have attentional deficits or problems with the executive functions, which may partially account for the behavioral problems that sometimes present.
Guzzetta They might have deficits in visioperceptive functioning. The child has difficulties recognizing the spatial relations between objects, as well as between objects and his own body. This results frequently in a constructive dyspraxia. The saccadic movement of the eye to focus on an object that appears periferically at the previous point of focus are slow and dyspraxic, which constitutes an added difficulty in order to achieve the perceptive integration.
The proprioceptive-visual integration of the parietal lobe is necessary in order to orient the movements and postures of the upper limbs to reach for and manipulate the surrounding objects and starting the proceeding automatic movement that experience and repetition offers.
These deficits are completely independent from the vision problems that may coexist Guzzetta Language problems are also common and their severity depends on the timing that the lesion took place, in the prelinguistic period or later, when the linguistic function has already started to form. Implications for everyday life Although there are many compelling reasons to give the diagnosis as early as possible parents frustration of handling a child with abnormal tone such as feeding, sleep, and temperament problems, plan in advance for long-term treatments and management options that may be needed by the child, possible increased insurance benefits and in some cases federal assistance, benefits that come from an early intervention the diagnosis should not be formally made until the second year of age.
The diagnosis has an impact on the life of the family and, of course, the child. The major issue of concern is, for most parents, walking. Children will CP will experience some degree of difficulty with movement. This can range from problems like clumsiness that does not disrupt everyday life activities all the way to difficulties with walking. The child may move slowly, may need to use a walking aid or a wheelchair. Simple activities like dressing, bathing, eating can be a real challenge to the child with CP and their family.
The activities can take longer, especially if the child needs more assistance, physical help or specialized equipment. Language problems are common among children with CP. Children may have difficulties with both verbal and non verbal aspects of language. Attentional processes The aim is for every child with CP to achieve their potential.
In order for the interface to be able to read the brain signal, the child needs to be focused. Not always is it possible for all children to emit a signal strong enough so that it can be captured by the interface.
The emission of a strong signal depends on the attention of the child which can be negatively affected by a variety of factors which have no relation with the interface but which affect its ability to read the brain signals.
How Brain-Computer Interfaces Work | HowStuffWorks
The attention of the child can be hindered by three main factors which are at constant interplay and affect the prefrontal cortex and the ability of the child to focus on a particular task. The three main factors are: Cognitive Emotional Behavioral Although the cognitive function of children with CP has not been systematically studied, and more research is needed, there is evidence suggesting that children with CP and normal intelligence present impairments in executive functions.
Executive functions are the brain functions that regulate and control impulse, anticipate consequences, put attention, regulate emotion, allow flexibility, plan and monitor results. Executive functions are highly fragile because they are the last cognitive area to mature. They involve the prefrontal cortex and they rely on an extensive interconnectivity with other parts of the brain.
Brain–computer interface - Wikipedia
Damage to that area results in slower information processing, and a decrementation in sustained attention performance, which is necessary for the reading of the signal by the interface Guzzetta In the case of an intellectual disability, which as we have seen affects almost half of the children with CP, we cannot speak of attention problems.
The degree of cognitive impairment is such that the attention processes cannot reach the required level so that it can be captured by the interface. The attentional processes are also going to be affected by the emotional problems that the child may be experiencing Parkes This is also an issue that has not been researched but there is enough evidence to suggest that children with CP, like children with some sort of a disability in general, are more likely to suffer from depression, anxiety and low self esteem.
Emotional problems hinder the ability of a child to focus and pay enough attention so as to send a strong signal to the interface.
Bottom of Form Children with CP have behavioral problems like being defiant and disobedient. The behavior problems reported by parents were 5 times more likely in children with cerebral palsy compared with children having no known health problems. Behavioral problem are associated with some kind of combination of the impairment, the environment and interpersonal relationships.
Damage to the prefrontal cortex affects, as we have seen, cognitive flexibility, the abilities for strategic planning, tolerance to frustration, behavioral inhibition hyperactivity-impulsivity as well as the associated impairment of inattention.
The child that has trouble maintaining his attention on the signal is more likely to refuse to try or abandon the task. It is important to mention epilepsy as one of the factors that cause behavior and attention difficulties in children with cerebral palsy.
Epilepsy, in itself, takes away part of the vitality of the brain, with the frequent crises affecting the cognitive abilities of the child. Behavior and attention difficulties are highly common in children with cerebral palsy who have epilepsy. Furthermore, the crises are frequently a motive for the child to stop receiving education.
Medical treatment for epilepsy can be helpful, keeping in mind that although antiepileptic drugs may impair the cognitive functions of the child, with the careful monitoring of the physician and the new medical intervention, this side effect would be very infrequent.
Communication system There are some limits that can be solved using the brain activity. This activity allows the communication between the processor and the person. Brain signals Through the recording and processing of direct brain electrical activity via signal processing and machine learning algorithms, BCIs enables communication and control to assistive devices. Although the aim of a BCI is to identify and translate brain electrical signals into commands, it is not a thought-reading device or systems able to literally translate arbitrary cognitive activities.
BCIs are design for translation of well characterized a priori defined brain activity patterns through the use of machine learning techniques and patterns recognition methods into commands. Considered as a control system, a BCI has an input e.
EEGan output e. Functional neuroimaging Invasively or noninvasively brain activity is recorded either from recording electrical activity through electrodes EEG, Electrocorticography ECoG or from single-neuron recordings within the brainrecording magnetic fields using magnetoencephalography MEGor recording metabolic activity reflected in changes in blood flow positron emission tomography PETfunctional magnetic resonance imaging fMRI and functional Near Infrared fNIR.
Invasive techniques such single-neuron recording and ECoG take recordings over the cortex; while single-neuron recording records the activity within the cortex, ECoG records the activity over the cortical surface of the brain. Single-neuron recordings and ECoG does not record single neuron activity but records activities over small regions of the brain giving them a high spatial resolution, and as it is implanted directly over the cortex, they have a high bandwidth, high SNR and high amplitude.
Since ECoG electrodes do not penetrate the cortex, recorded signals are also not subjected as heavily to immune response, possess lower risk to implant as well. Furthermore, maintaining long term reliable recording with implantable electrodes is difficult.
EMG, EOG or electrical devicesEEG has become the most common source for brain activity due to its none invasiveness requiring no craniotomy surgical incision of the skullbeing more practical for everyday situations. EEG measures the potential over the scalp, reflecting the collective activity over large population of neurons located underneath the sensor position.
Recording and processing Brain signal recordings, like EEG or ECoG, are obtained with electrodes attach from the surface of the skull or to the surface of the brain measuring difference over the potential that reflect the activity within the brain.
The electrodes are connected to biosignal amplifier where they are amplified and go through an analog-digital conversion. These signals are sent to the signal processing system that is in charge to perform the feature extraction and classification.
Finally, a signal will be send to the control system as final output. The electrodes measure a difference in potential i.
The difference in potential reflects neural activity below the electrode. There are different EEG electrode montages. Usual EEG recordings use unipolar montage rather than bipolar electrodes, meaning that they use a common reference for all electrodes. A ground is added to keep the voltage levels close to the amplifier ground voltage level.
Brain Computer Interfaces for Cerebral Palsy
The reference and ground can be positioned everywhere within the array of electrodes, but they are normally placed either over the ear or the mastoids the temporal bone behind the ear. Task switching during a period of sustained concentration can be counter productive. The BCI can detect oncoming, naturally occurring dips in attention during the performance of the primary task and induce a switch to the secondary task.
Therapeutics There are many disorders of attention that can be addressed with the proposed BCI system, including autism, mood disorders, and brain injury. Conclusions Team 7B designed a general framework for a human-centered BCI that can be useful for education, enhancing every day life and providing therapeutic interventions. Though we focused on one specific cognitive function, i.
Though the team did not discuss specifics about when BCI systems to control cognitive functions may be ready for testing in humans, most of the system components already exist in some form. Over the past few years, flexible electrode arrays have been implanted in human patients to detect the onset of epileptic seizures. Similar technology could be implanted within the brain areas discussed earlier to control some aspects of attention via electrical stimulation.
Page 92 Share Cite Suggested Citation: Here, stimulation is delivered with electrodes placed on the scalp above the target brain area. However, further advances are needed to provide stimulation to precise locations in the brain. Because team members had a rich collective expertise in restorative systems seeking to restore lost sensory or motor abilities in a clinical settingearly discussion focused on the state of the art and the technical obstacles to effective motor control through BCI. To explore the outer limits of current technology for decoding information from brains, the team tried to envision scenarios of direct brain-to-brain communication.
This technology might even enhance our social lives in a digital age where communication is increasingly carried out in an emotionally restrictive online environment. However, questions of implementation quickly overshadowed the theoretical discussion. The extreme challenges of decoding neural signals into relevant information, maintaining the integrity of implanted interfaces over time, and generalizing among unique, individual brains all came to the fore.
Page 93 Share Cite Suggested Citation: In the end, the technical discussion laid a meaningful groundwork for developing a more creative application of BCI. Lessons from Neural Prosthetics: First is the question of invasive versus non-invasive technology. The team agreed that much information can be gathered from outside the brain, without the surgical implantation of electronics, but that such information can serve only very specific, limited purposes. The skull is ultimately a powerful insulator of signals, and the only way to record or stimulate precise neurons or neural populations is by opening the skull and interacting directly with brain tissue.
However, such a radical procedure has limited potential for use in humans, particularly those not seeking solutions to a severe physical disability. In fact, even among amputees and paralyzed individuals, resistance to invasive BCI is common. A second source of tension in the field concerns human physiology. History[ edit ] The history of brain—computer interfaces BCIs starts with Hans Berger 's discovery of the electrical activity of the human brain and the development of electroencephalography EEG.
In Berger was the first to record human brain activity by means of EEG. Berger's first recording device was very rudimentary. He inserted silver wires under the scalps of his patients.
These were later replaced by silver foils attached to the patient's head by rubber bandages.
Berger connected these sensors to a Lippmann capillary electrometerwith disappointing results. However, more sophisticated measuring devices, such as the Siemens double-coil recording galvanometerwhich displayed electric voltages as small as one ten thousandth of a volt, led to success.
Berger analyzed the interrelation of alternations in his EEG wave diagrams with brain diseases. EEGs permitted completely new possibilities for the research of human brain activities. Although the term had not yet been coined, one of the earliest examples of a working brain-machine interface was the piece Music for Solo Performer by the American composer Alvin Lucier. The piece makes use of EEG and analog signal processing hardware filters, amplifiers, and a mixing board to stimulate acoustic percussion instruments.
To perform the piece one must produce alpha waves and thereby "play" the various percussion instruments via loudspeakers which are placed near or directly on the instruments themselves. His paper stated the "BCI challenge": Control of objects using EEG signals. The demonstration was movement in a maze. In report was given on noninvasive EEG control of a physical object, a robot. The experiment described was EEG control of multiple start-stop-restart of the robot movement, along an arbitrary trajectory defined by a line drawn on a floor.
The line-following behavior was the default robot behavior, utilizing autonomous intelligence and autonomous source of energy. The obtained cognitive wave representing the expectation learning in the brain is named Electroexpectogram EXG. Neuroprosthetics Neuroprosthetics is an area of neuroscience concerned with neural prostheses, that is, using artificial devices to replace the function of impaired nervous systems and brain related problems, or of sensory organs.
The most widely used neuroprosthetic device is the cochlear implant which, as of Decemberhad been implanted in approximatelypeople worldwide. The difference between BCIs and neuroprosthetics is mostly in how the terms are used: Practical neuroprosthetics can be linked to any part of the nervous system—for example, peripheral nerves—while the term "BCI" usually designates a narrower class of systems which interface with the central nervous system.
The terms are sometimes, however, used interchangeably. Neuroprosthetics and BCIs seek to achieve the same aims, such as restoring sight, hearing, movement, ability to communicate, and even cognitive function.
Animal BCI research[ edit ] Several laboratories have managed to record signals from monkey and rat cerebral cortices to operate BCIs to produce movement.
Monkeys have navigated computer cursors on screen and commanded robotic arms to perform simple tasks simply by thinking about the task and seeing the visual feedback, but without any motor output. In the s, Apostolos Georgopoulos at Johns Hopkins University found a mathematical relationship between the electrical responses of single motor cortex neurons in rhesus macaque monkeys and the direction in which they moved their arms based on a cosine function.
He also found that dispersed groups of neurons, in different areas of the monkey's brains, collectively controlled motor commands, but was able to record the firings of neurons in only one area at a time, because of the technical limitations imposed by his equipment. Prominent research successes[ edit ] Kennedy and Yang Dan[ edit ] Phillip Kennedy who later founded Neural Signals in and colleagues built the first intracortical brain—computer interface by implanting neurotrophic-cone electrodes into monkeys.
Researchers targeted brain cells in the thalamus lateral geniculate nucleus area, which decodes signals from the retina. The cats were shown eight short movies, and their neuron firings were recorded.
Using mathematical filters, the researchers decoded the signals to generate movies of what the cats saw and were able to reconstruct recognizable scenes and moving objects. Nicolelis[ edit ] Miguel Nicolelisa professor at Duke Universityin Durham, North Carolinahas been a prominent proponent of using multiple electrodes spread over a greater area of the brain to obtain neuronal signals to drive a BCI. After conducting initial studies in rats during the s, Nicolelis and his colleagues developed BCIs that decoded brain activity in owl monkeys and used the devices to reproduce monkey movements in robotic arms.
Monkeys have advanced reaching and grasping abilities and good hand manipulation skills, making them ideal test subjects for this kind of work. By the group succeeded in building a BCI that reproduced owl monkey movements while the monkey operated a joystick or reached for food.
But the monkeys could not see the arm moving and did not receive any feedback, a so-called open-loop BCI. Diagram of the BCI developed by Miguel Nicolelis and colleagues for use on rhesus monkeys Later experiments by Nicolelis using rhesus monkeys succeeded in closing the feedback loop and reproduced monkey reaching and grasping movements in a robot arm.
With their deeply cleft and furrowed brains, rhesus monkeys are considered to be better models for human neurophysiology than owl monkeys.
The monkeys were trained to reach and grasp objects on a computer screen by manipulating a joystick while corresponding movements by a robot arm were hidden. The BCI used velocity predictions to control reaching movements and simultaneously predicted handgripping force. The monkey was brain controlling the position of an avatar arm while receiving sensory feedback through direct intracortical stimulation ICMS in the arm representation area of the sensory cortex.