{"product_id":"brain-computer-interfaces-a-new-era-in-treating-brain-and-mental-health-disorders","title":"Brain–Computer Interfaces: A New Era in Treating Brain and Mental Health Disorders","description":"\u003cp\u003eBrain–computer interface (BCI) technology lets people control devices with their own brain signals. This review describes how BCI has developed since the 1800s. This review describes how different BCI systems are classified. This review describes how BCI systems are being tested for movement disorders, communication loss, and psychiatric conditions such as autism and depression. The authors report that BCI still faces major hurdles, including long-term electrode safety and limited signal resolution. The authors highlight new electrode materials, endovascular BCI, optogenetics, and artificial intelligence as promising next steps.\u003c\/p\u003e\n\n\u003ch1\u003eBrain–Computer Interfaces: A New Era in Treating Brain and Mental Health Disorders\u003c\/h1\u003e\n\n\u003ch2\u003eTable of Contents\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"#ddn-key-points\"\u003eKey Points\u003c\/a\u003e\u003c\/li\u003e\n\n  \u003cli\u003e\u003ca href=\"#background\"\u003eBackground: Why Brain–Computer Interfaces Matter\u003c\/a\u003e\u003c\/li\u003e\n  \u003cli\u003e\u003ca href=\"#history\"\u003eThe History of Brain–Computer Interfaces\u003c\/a\u003e\u003c\/li\u003e\n  \u003cli\u003e\u003ca href=\"#classification\"\u003eHow Brain–Computer Interfaces Are Classified\u003c\/a\u003e\u003c\/li\u003e\n  \u003cli\u003e\u003ca href=\"#movement\"\u003eTreating Movement Disorders with Brain–Computer Interfaces\u003c\/a\u003e\u003c\/li\u003e\n  \u003cli\u003e\u003ca href=\"#communication\"\u003eHelping Patients with Communication Barriers\u003c\/a\u003e\u003c\/li\u003e\n  \u003cli\u003e\u003ca href=\"#psychiatric\"\u003ePsychiatric Conditions: Diagnosis and Treatment\u003c\/a\u003e\u003c\/li\u003e\n  \u003cli\u003e\u003ca href=\"#challenges\"\u003eChallenges and Limitations\u003c\/a\u003e\u003c\/li\u003e\n  \u003cli\u003e\u003ca href=\"#future\"\u003eFuture Directions and Emerging Technologies\u003c\/a\u003e\u003c\/li\u003e\n  \u003cli\u003e\u003ca href=\"#implications\"\u003eWhat This Means for Patients\u003c\/a\u003e\u003c\/li\u003e\n  \u003cli\u003e\u003ca href=\"#recommendations\"\u003eRecommendations and Takeaways\u003c\/a\u003e\u003c\/li\u003e\n  \u003cli\u003e\u003ca href=\"#ddn-faq\"\u003eFrequently Asked Questions\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"#source\"\u003eSource Information\u003c\/a\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003c!-- ddn:keypoints:start --\u003e\n\u003ch2 id=\"ddn-key-points\"\u003eKey Points\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eBCI lets people control devices with their own brain signals and can provide feedback for rehabilitation and closed-loop neuromodulation.\u003c\/li\u003e\n\u003cli\u003eNon-invasive BCIs are safer and less expensive but have poorer signal quality; invasive BCIs have the highest signal-to-noise ratio but require surgery.\u003c\/li\u003e\n\u003cli\u003eBCI is being tested for movement disorders, communication loss, and psychiatric conditions such as autism and depression.\u003c\/li\u003e\n\u003cli\u003eChallenges include long-term electrode safety, limited signal resolution, portability, and the need for better personalized signal decoding.\u003c\/li\u003e\n\u003cli\u003eNew electrode materials, endovascular BCI, optogenetics, and artificial intelligence integration are expected to enhance future BCI systems.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c!-- ddn:keypoints:end --\u003e\n\n\n\u003ch2 id=\"background\"\u003eBackground: Why Brain–Computer Interfaces Matter\u003c\/h2\u003e\n\n\u003cp\u003eNeurological and psychiatric diseases are increasing worldwide. Population aging, longer life expectancy, and psychological stress all drive this rise. These conditions place heavy social and economic burdens on families, healthcare systems, and economies.\u003c\/p\u003e\n\n\u003cp\u003eThe numbers are staggering. According to 2021 statistics from the Global Health Data Exchange, an estimated \u003cstrong\u003e2.87 billion people\u003c\/strong\u003e worldwide are affected by neurological diseases. These include age-related dementias, neurodegenerative diseases, strokes, and traumatic brain injuries. Another \u003cstrong\u003e1.09 billion people\u003c\/strong\u003e suffer from psychiatric conditions such as depression.\u003c\/p\u003e\n\n\u003cp\u003eDepression alone reached \u003cstrong\u003e320 million cases\u003c\/strong\u003e globally in 2021. That equals \u003cstrong\u003e43 cases per 1,000 individuals\u003c\/strong\u003e. Parkinson's disease (PD) affects \u003cstrong\u003e149 individuals per 100,000\u003c\/strong\u003e worldwide. Parkinson's disease has led to the loss of an estimated \u003cstrong\u003e7.47 million healthy life years\u003c\/strong\u003e.\u003c\/p\u003e\n\n\u003cp\u003eDementia cases from Alzheimer's disease (AD) and other causes approach \u003cstrong\u003e55 million\u003c\/strong\u003e globally. In the same year, there were about \u003cstrong\u003e12 million stroke incidents\u003c\/strong\u003e worldwide. Among stroke survivors, \u003cstrong\u003e53.4%\u003c\/strong\u003e experienced post-stroke cognitive impairment.\u003c\/p\u003e\n\n\u003cp\u003eThese disorders often interact with each other. The underlying neuropathological mechanisms can worsen disability. In summary, neurological and psychiatric diseases have become a major global public health crisis. This crisis demands urgent development of innovative therapies.\u003c\/p\u003e\n\n\u003cp\u003eHowever, the adult human brain has limited repair capacity. Neurological damage remains largely irreparable despite decades of intensive research in regenerative therapies.\u003c\/p\u003e\n\n\u003cp\u003eBrain–computer interface (BCI) technology is a promising research direction for treating these diseases. BCI systems provide users with brain output channels. These channels convert neural activity into control instructions for external devices. BCI can also provide input signals as motor, tactile, or visual feedback. This feedback supports rehabilitation training and enables personalized, closed-loop neuromodulation therapies.\u003c\/p\u003e\n\n\u003cp\u003eThe emergence of BCI is redefining the boundaries of modern medicine. It provides a new technical path for treating neurological and psychiatric diseases.\u003c\/p\u003e\n\n\u003cp\u003eA typical BCI system has three key components:\u003c\/p\u003e\n\u003cul\u003e\n  \u003cli\u003eA signal acquisition module\u003c\/li\u003e\n  \u003cli\u003eA signal processing module\u003c\/li\u003e\n  \u003cli\u003eControllable external devices\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003cp\u003eCurrently, BCI in medicine focuses on neuropsychiatric diseases. Its uses include disease assessment, functional assistance, rehabilitation training, and neuromodulation.\u003c\/p\u003e\n\n\u003cp\u003eFor diagnosis, BCI systems can detect the onset of neurological impairments or events such as seizures. For functional assistance, BCI-generated output signals let patients with strokes, limb defects, or spinal cord injuries (SCIs) control external devices such as wheelchairs and prosthetics. For rehabilitation, BCI provides essential feedback and training paradigms. BCI can also trigger neuromodulatory inputs such as deep brain stimulation (DBS) to suppress seizures or regulate movement in Parkinson's disease.\u003c\/p\u003e\n\n\u003cp\u003eBCI systems can also be designed to evaluate and treat psychiatric disorders. These include depression, autism, attention deficit hyperactivity disorder (ADHD), and bipolar disorder. Treatment may use stimulation-based or pharmacological interventions.\u003c\/p\u003e\n\n\u003cp\u003eDespite this promise, several challenges remain. These include risks from implanting signal detectors and stimulators, limited system portability, and the need for better neural signal decoding for personalized device control.\u003c\/p\u003e\n\n\u003cp\u003eThis review summarizes recent advances in BCI system design for neuropsychiatric diseases. It covers signal detection and decoding, feedback control for external devices and neuromodulation, technical achievements in system engineering and fabrication, and potential future applications.\u003c\/p\u003e\n\n\u003ch2 id=\"history\"\u003eThe History of Brain–Computer Interfaces\u003c\/h2\u003e\n\n\u003cp\u003eThe development of BCI systems traces back to the discovery of bioelectric phenomena in the 19th century. In 1875, British physicist Richard Caton was the first to record electrical activity from animal brains. He published his findings in the British Medical Journal. This discovery paved the way for routine clinical use of electrophysiological measurements. These include the electroencephalogram (EEG), electrocardiogram, electromyogram (EMG), and compound nerve action potential recordings. It also laid the foundation for BCI technology.\u003c\/p\u003e\n\n\u003cp\u003eIn 1913, Prawdicz-Neminski was the first to capture seven distinct patterns of bioelectrical activity in animal brains. He called them components of the \"electrocerebrogram.\" However, he misinterpreted these signals as artifacts from human interference. He did not recognize them as originating from biological processes.\u003c\/p\u003e\n\n\u003cp\u003eIn 1924, Hans Berger successfully recorded the first human EEG. He identified endogenous oscillating waveforms, now recognized as α- and β-waves. This milestone provided a crucial theoretical foundation for BCI technology.\u003c\/p\u003e\n\n\u003cp\u003eThe mid-20th century brought significant progress. In 1964, Walter et al. first measured slow cortical potentials (SCPs). These were later adapted to enable communication for patients with locked-in syndrome (LIS). LIS is a condition of full awareness but complete paralysis, also known as pseudocoma.\u003c\/p\u003e\n\n\u003cp\u003eIn 1969, Eberhard Fetz conducted the first BCI experiments with monkeys. This marked a pivotal breakthrough. Around the same period, M.B. Sterman identified the sensorimotor rhythm (μ-rhythm) in feline brains.\u003c\/p\u003e\n\n\u003cp\u003eDespite these advances, the term \"brain–computer interface\" was not formally introduced until 1973. Jacques J. Vidal of UCLA developed a system based on visual event-related potentials (ERPs). This established BCI as a distinct research field.\u003c\/p\u003e\n\n\u003cp\u003eA landmark achievement followed in 1978. William Dobelle implanted a 68-electrode array in a blind patient's visual cortex. Electrical stimulation successfully induced phosphenes, which are light perceptions without actual light input. This represented the first major human application of BCI technology.\u003c\/p\u003e\n\n\u003cp\u003eThese achievements were followed by breakthroughs in BCI signal detection and processing. In 1988, Farwell and Donchin proposed the famous \"P300 speller\" paradigm. This allowed individuals with severe motor impairments to communicate. They identified selected letters on a screen through the P300 ERP waveform associated with decision-making.\u003c\/p\u003e\n\n\u003cp\u003eIn the early 1990s, Pfurtscheller and colleagues expanded BCI application. They used event-related desynchronization (ERD) and event-related synchronization as BCI control signals. In 1992, Erich E. Sutter developed a BCI system based on visual evoked potentials (VEPs). In 1996, McMillan et al. introduced a system using steady-state visual evoked potentials (SSVEPs).\u003c\/p\u003e\n\n\u003cp\u003eFurther advancements were made in applying BCI to control motor devices. In 1998, Philip Kennedy and Roy Bakay of Emory University implanted a \"motor neuroprosthesis\" in a patient with LIS. This enabled control of a computer cursor using BCI. In 1999, the first BCI International Conference was held. This marked a key step toward systematic classification and standardization of BCI technology. In 2000, Jonathan Wolpaw proposed a comprehensive definition of BCI, laying a theoretical foundation for subsequent research.\u003c\/p\u003e\n\n\u003cp\u003eBCI system development accelerated in the early 21st century. In 2004, Matt Nagle was the first patient to use an invasive BCI system to control a robotic arm. This marked a major breakthrough in clinical application of BCI technology. Since then, experimental and clinical applications have expanded rapidly. Over the past decade, numerous BCI-based applications for humans have been developed and tested. These have attracted increasing attention from academia and news media.\u003c\/p\u003e\n\n\u003ch2 id=\"classification\"\u003eHow Brain–Computer Interfaces Are Classified\u003c\/h2\u003e\n\n\u003cp\u003eThe general goal of BCI systems is to enable direct interactions between the human brain and external devices. This can be achieved in several ways. Neural signals can be converted into specific output commands, such as for motor prosthesis control. External stimuli can be converted into brain signals for artificial sensory perception. Reciprocal communication can also be facilitated, as seen in sensorimotor feedback systems.\u003c\/p\u003e\n\n\u003cp\u003eBCI systems can be categorized based on signal measurement modality, application, and invasiveness. Each category has distinct advantages and limitations in signal acquisition and processing.\u003c\/p\u003e\n\n\u003cp\u003eAccording to the signal source, BCIs can be divided into SSVEP-based, P300-based, and motor imagery (MI)-based types.\u003c\/p\u003e\n\n\u003cul\u003e\n  \u003cli\u003e\n\u003cstrong\u003eSteady-state visual evoked potentials (SSVEPs)\u003c\/strong\u003e are brain waves produced in synchrony with visual stimuli. A user can generate control signals at a specific frequency in response to visual targets presented at that frequency.\u003c\/li\u003e\n  \u003cli\u003e\n\u003cstrong\u003eThe P300 potential\u003c\/strong\u003e is a transient ERP produced when the brain receives an unexpected stimulus.\u003c\/li\u003e\n  \u003cli\u003e\n\u003cstrong\u003eMotor imagery (MI)\u003c\/strong\u003e refers to electrical signals generated when imagining the preparation or performance of a movement.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003cp\u003eThese signals can be further divided into evoked and spontaneous signals. Evoked activity refers to neural electrical potentials generated in the brain upon external stimuli, such as SSVEPs and P300 waveforms. Spontaneous activity refers to neural activity generated by the user, such as MI.\u003c\/p\u003e\n\n\u003cp\u003eOther BCI signal paradigms are also worth exploring. These include error-related negativity (ERN), movement-related cortical potential (MRCP), SCP, and ERD. These paradigms have found particular utility in specific applications.\u003c\/p\u003e\n\n\u003cul\u003e\n  \u003cli\u003eThe \u003cstrong\u003eERN\u003c\/strong\u003e reflects a neural mechanism for performance monitoring and error correction. Larger ERN amplitudes correlate with enhanced error detection and compensatory behaviors.\u003c\/li\u003e\n  \u003cli\u003eThe \u003cstrong\u003eERD\u003c\/strong\u003e involves attenuation of EEG power in the alpha and beta frequency bands before movement. It is widely applied in motor imagery and execution tasks.\u003c\/li\u003e\n  \u003cli\u003eThe \u003cstrong\u003eMRCP\u003c\/strong\u003e exhibits distinct spatiotemporal patterns during movement preparation and execution. Studies have combined MRCP and ERD to elucidate the temporal dynamics of motor control.\u003c\/li\u003e\n  \u003cli\u003eThe \u003cstrong\u003eSCP\u003c\/strong\u003e has been investigated for its role in SCP modulation, providing a low-frequency alternative for BCI control.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003cp\u003eDepending on the location of electrode implantation, BCIs can be divided into invasive, semi-invasive, and non-invasive types.\u003c\/p\u003e\n\n\u003cp\u003eElectrodes for noninvasive BCI are placed outside the skin. Examples include conventional EEG recording and magnetoencephalography (MEG). Electroencephalographic electrodes record electrical activity generated by vertical currents on the scalp surface. MEG measures the magnetic field generated by a tangential current.\u003c\/p\u003e\n\n\u003cp\u003eA semi-invasive BCI is an intermediate technology between non-invasive and fully invasive approaches. Its electrodes are implanted inside the skull but on the surface of the cerebral cortex (epidurally or subdurally). They do not penetrate the brain parenchyma. Electrodes can be surgically placed on the cortical surface, such as via electrocorticography (ECoG). They can also be minimally injected as flexible components, such as hydrogel or mesh-like structures, into target brain regions.\u003c\/p\u003e\n\n\u003cp\u003eThis approach combines advantages of non-invasive methods (lower surgical risk) and invasive methods (higher signal quality). The absence of parenchymal penetration provides significantly superior spatial resolution compared to EEG. It also poses lower immunological response risks than fully implanted electrodes.\u003c\/p\u003e\n\n\u003cp\u003eIn the most invasive configuration, multi-unit EEG electrode arrays are implanted on or beneath the cortical surface. These capture neuronal signals generated near the electrode tips. ECoG electrodes can be placed on the cerebral cortex surface to record synchronized neuronal activity. They can also be implanted into gray matter, depending on the application scenario.\u003c\/p\u003e\n\n\u003cp\u003eNon-invasive BCIs such as EEG-based systems are the safest, most secure, and least expensive. They require no surgical implantation. However, the signal-to-noise ratio (SNR) and spatial resolution are comparatively poor. Signals can be easily distorted by external signals and movement.\u003c\/p\u003e\n\n\u003cp\u003eInvasive BCI systems achieve the highest SNR. However, they require implantation surgery and may trigger an inflammatory response at the implantation site. Semi-invasive BCI strikes a balance between the two.\u003c\/p\u003e\n\n\u003cp\u003eNon-invasive electrodes can be further divided into dry, wet, and semi-dry electrodes. This depends on whether conductive pastes are required.\u003c\/p\u003e\n\n\u003cul\u003e\n  \u003cli\u003e\n\u003cstrong\u003eDry electrodes\u003c\/strong\u003e do not require conductive pastes. However, higher input impedance at the skin surface results in relatively poor signal quality.\u003c\/li\u003e\n  \u003cli\u003e\n\u003cstrong\u003eWet electrodes\u003c\/strong\u003e use conductive pastes to reduce skin impedance, improve signal quality, and reduce motion artifacts. However, their adhesion may require removal of underlying hair and cuticle, resulting in skin damage. Conductive pastes can also dry out from evaporation, decreasing conductivity. Wet electrodes are not appropriate for long-term signal recording.\u003c\/li\u003e\n  \u003cli\u003e\n\u003cstrong\u003eSemi-dry electrodes\u003c\/strong\u003e combine the ease of use of dry electrodes with the higher signal quality of wet electrodes. They are typically made of flexible polymer materials or nano-metal porous materials. They contain electrolytes that are automatically released and penetrate the stratum corneum (outermost epidermal layer) during use. This reduces impedance and improves signal quality.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003cp\u003eBCI systems can be classified as open loop or closed loop based on feedback mechanisms.\u003c\/p\u003e\n\n\u003cp\u003eOpen-loop BCIs analyze brain states in real-time to control external devices. However, they lack bidirectional signal flow, limiting adaptive adjustment. For example, the P300 speller paradigm enables communication by detecting ERPs in response to visual stimuli. However, it does not integrate user feedback to optimize performance. This system is suitable for static tasks like letter selection. It may struggle with dynamic adjustments.\u003c\/p\u003e\n\n\u003cp\u003eIn contrast, closed-loop BCIs integrate real-time feedback to modulate stimulation parameters or decoding strategies. These systems typically use neurofeedback (NF) to analyze brain activity. They then initiate neuroplasticity or modulate brain activity through brain stimulation techniques.\u003c\/p\u003e\n\n\u003cp\u003eFor instance, adaptive deep brain stimulation (aDBS) systems dynamically adjust stimulation intensity. They do this based on subthalamic nucleus local field potentials (STN-LFPs) in Parkinson's disease patients. This reduces motor fluctuations and improves therapy precision.\u003c\/p\u003e\n\n\u003cp\u003eAnother BCI system based on EEG incorporates adaptive closed-loop control algorithms. These automatically adjust feedback patterns and control strategies according to individual differences and training progress. This achieves more effective rehabilitation training.\u003c\/p\u003e\n\n\u003cp\u003eClosed-loop systems demonstrate higher decoding accuracy and personalized intervention. However, they require more complex hardware and risk feedback-induced neural adaptation.\u003c\/p\u003e\n\n\u003ch2 id=\"movement\"\u003eTreating Movement Disorders with Brain–Computer Interfaces\u003c\/h2\u003e\n\n\u003cp\u003eMovement disorders are the primary manifestations of many neurological diseases. Patients with Parkinson's disease, for instance, suffer from progressively worsening motor symptoms. These include tremors, stiffness, and gait disturbances.\u003c\/p\u003e\n\n\u003cp\u003eMedications can relieve these symptoms in the early stages. However, they usually lose efficacy with disease progression. BCI systems can be designed to monitor activity in the motor cortex. They decode these signals with the assistance of various algorithms, including machine learning. They then send decoded commands to control external devices such as robotic arms and stimulators.\u003c\/p\u003e\n\n\u003cp\u003eSince Matt Nagle became the first patient to use an invasive BCI system to control a robotic arm in 2004, BCI use for movement disorders has shown increasing promise. In recent decades, many new technologies have been developed for the brains or limbs of patients. This section describes BCI systems for movement disorders according to the technologies applied rather than specific diseases. This is because treatment methods often overlap.\u003c\/p\u003e\n\n\u003ch3\u003eProsthetics and Robotic Arms\u003c\/h3\u003e\n\n\u003cp\u003eSpinal cord injuries (SCIs) can result in life-altering paraplegia or quadriplegia. For patients with these symptoms, BCI systems using implanted electrodes can record neural activity from the motor cortex. They decode this activity into control signals interpretable by prosthetic limbs and other assistive devices.\u003c\/p\u003e\n\n\u003cp\u003eIn addition to unidirectional open-loop systems for external device control, a bidirectional BCI system has been described for an SCI-induced paraplegia male patient. This system not only decodes neural activity from the motor cortex to control a robotic arm. It also provides tactile feedback by delivering intracortical microstimulation to the sensory cortex for enhanced control.\u003c\/p\u003e\n\n\u003cp\u003eIn another application, a subcortical stroke patient implanted with the BCI system was able to decode EEG signals associated with imagined hand movements. The patient then controlled an exoskeleton for opening and closing actions.\u003c\/p\u003e\n\n\u003cp\u003eFurther advancements have been made in decoding brain signals to control prosthetic fingers. A hybrid system integrates motor imagery (MI) with other signal detection modalities. By integrating these modalities, the hybrid system effectively mitigates the inherent limitations of each modality. For example, it addresses the low temporal resolution of functional near-infrared spectroscopy (fNIRS). This significantly enhances the precision and functionality of the prosthetic device. This advancement offers patients a more natural and flexible movement experience.\u003c\/p\u003e\n\n\u003cp\u003eAdditionally, the development of a portable and modular BCI software platform has created new possibilities for home-based applications. For instance, this platform enables control of a mechanical glove for grasping as an output device via a smartphone application. This innovation simplifies the operation of BCI systems. It also offers a more accessible rehabilitation tool for paralyzed patients, significantly improving functional independence.\u003c\/p\u003e\n\n\u003ch2 id=\"communication\"\u003eHelping Patients with Communication Barriers\u003c\/h2\u003e\n\n\u003cp\u003eBCI technology offers alternative communication methods for patients who cannot speak or move. This includes patients with locked-in syndrome (LIS), a condition of full awareness but complete paralysis. The P300 speller paradigm, developed in 1988, was an early example. It allowed individuals with severe motor impairments to communicate by identifying selected letters on a screen.\u003c\/p\u003e\n\n\u003cp\u003eBCI systems can also decode speech-related neural signals. This enables communication through brain activity rather than physical movement. These applications represent a significant advance for patients with communication barriers.\u003c\/p\u003e\n\n\u003ch2 id=\"psychiatric\"\u003ePsychiatric Conditions: Diagnosis and Treatment\u003c\/h2\u003e\n\n\u003cp\u003eBCI shows growing potential in both diagnosis and treatment of psychiatric conditions. This is particularly true for conditions like autism and depression. BCI systems can be designed for evaluation and treatment through stimulation-based or pharmacological interventions.\u003c\/p\u003e\n\n\u003cp\u003eThese systems can also be applied to other psychiatric disorders. These include attention deficit hyperactivity disorder (ADHD) and bipolar disorder. The ability to monitor brain activity in real-time offers new possibilities for personalized treatment.\u003c\/p\u003e\n\n\u003ch2 id=\"challenges\"\u003eChallenges and Limitations\u003c\/h2\u003e\n\n\u003cp\u003eDespite significant progress, BCI faces several challenges. The long-term biocompatibility of electrodes remains to be improved. This means electrodes must be safe for long-term use inside the body without causing harm. The resolution of neural signals also remains to be improved.\u003c\/p\u003e\n\n\u003cp\u003eOther challenges include risks associated with signal detector and stimulator implantation. Limited system portability is another issue. There is also a need for improved neural signal decoding for personalized device control.\u003c\/p\u003e\n\n\u003cp\u003eInvasive BCI systems achieve the highest signal-to-noise ratio. However, they require implantation surgery and may trigger an inflammatory response at the implantation site. Non-invasive BCIs are safer and less expensive. However, their signal-to-noise ratio and spatial resolution are comparatively poor. Signals can be easily distorted by external signals and movement.\u003c\/p\u003e\n\n\u003cp\u003eClosed-loop systems demonstrate higher decoding accuracy and personalized intervention. However, they require more complex hardware. They also risk feedback-induced neural adaptation.\u003c\/p\u003e\n\n\u003ch2 id=\"future\"\u003eFuture Directions and Emerging Technologies\u003c\/h2\u003e\n\n\u003cp\u003eTo address current limitations, research on new electrode materials is ongoing. These include carbon nanomaterials and composites. Emerging BCI technologies present new possibilities. These include endovascular BCI and optogenetics BCI.\u003c\/p\u003e\n\n\u003cp\u003eThe integration of multimodal technologies and artificial intelligence in BCI systems is expected to enhance performance. This integration should also enable more personalized treatment.\u003c\/p\u003e\n\n\u003cp\u003eOverall, BCI technology holds great promise for improving the quality of life of patients with neuropsychiatric disorders. It also drives innovation in the medical and neuroscience fields.\u003c\/p\u003e\n\n\u003ch2 id=\"implications\"\u003eWhat This Means for Patients\u003c\/h2\u003e\n\n\u003cp\u003eFor patients with movement disorders such as Parkinson's disease, BCI offers a potential path to regain motor function. This includes control of prosthetic limbs, robotic arms, and exoskeletons. It also includes brain stimulation-based therapies that can reduce motor fluctuations.\u003c\/p\u003e\n\n\u003cp\u003eFor patients with communication barriers, BCI enables alternative communication methods. This includes speech-related neural signal decoding. This could be life-changing for patients with locked-in syndrome or severe motor impairments.\u003c\/p\u003e\n\n\u003cp\u003eFor patients with psychiatric conditions such as autism and depression, BCI shows growing potential in both diagnosis and treatment. This represents a new frontier in mental health care.\u003c\/p\u003e\n\n\u003cp\u003eThe numbers speak to the scale of need. With 2.87 billion people affected by neurological diseases and 1.09 billion by psychiatric conditions, the development of effective BCI treatments could have enormous impact. Depression alone reached 320 million cases in 2021. Parkinson's disease affects 149 per 100,000 people worldwide. Dementia cases approach 55 million. Stroke incidents reached about 12 million in one year, with 53.4% of survivors experiencing cognitive impairment.\u003c\/p\u003e\n\n\u003ch2 id=\"recommendations\"\u003eRecommendations and Takeaways\u003c\/h2\u003e\n\n\u003cp\u003eBased on this review, several key takeaways emerge for patients and caregivers:\u003c\/p\u003e\n\n\u003col\u003e\n  \u003cli\u003e\n\u003cstrong\u003eBCI is an emerging field with real clinical progress.\u003c\/strong\u003e Since the first human EEG in 1924 and the first BCI-controlled robotic arm in 2004, the technology has advanced rapidly.\u003c\/li\u003e\n  \u003cli\u003e\n\u003cstrong\u003eDifferent BCI types suit different needs.\u003c\/strong\u003e Non-invasive BCIs are safer but have lower signal quality. Invasive BCIs have the best signal quality but require surgery. Semi-invasive BCIs balance these trade-offs.\u003c\/li\u003e\n  \u003cli\u003e\n\u003cstrong\u003eMovement disorders are a leading application.\u003c\/strong\u003e Patients with spinal cord injuries, strokes, and Parkinson's disease have used BCI to control prosthetics, exoskeletons, and robotic arms.\u003c\/li\u003e\n  \u003cli\u003e\n\u003cstrong\u003eCommunication barriers can be addressed.\u003c\/strong\u003e BCI enables alternative communication for patients who cannot speak or move.\u003c\/li\u003e\n  \u003cli\u003e\n\u003cstrong\u003ePsychiatric applications are growing.\u003c\/strong\u003e BCI shows potential for diagnosing and treating autism, depression, ADHD, and bipolar disorder.\u003c\/li\u003e\n  \u003cli\u003e\n\u003cstrong\u003eChallenges remain.\u003c\/strong\u003e Long-term electrode safety, signal resolution, portability, and personalized decoding all need improvement.\u003c\/li\u003e\n  \u003cli\u003e\n\u003cstrong\u003eNew technologies are on the horizon.\u003c\/strong\u003e Carbon nanomaterial electrodes, endovascular BCI, optogenetics, and artificial intelligence integration are expected to enhance future BCI systems.\u003c\/li\u003e\n\u003c\/ol\u003e\n\n\u003cp\u003ePatients interested in BCI should discuss options with their neurologist or psychiatrist. Clinical availability varies by condition and region. Most BCI applications remain in experimental or early clinical stages.\u003c\/p\u003e\n\n\u003c!-- ddn:faq:start --\u003e\n\u003ch2 id=\"ddn-faq\"\u003eFrequently Asked Questions\u003c\/h2\u003e\n\u003ch3\u003eWhat is a brain–computer interface, or BCI?\u003c\/h3\u003e\n\u003cp\u003eA BCI is a system that lets a person control devices using their own brain signals. It converts neural activity into control instructions for external devices, and can also send signals back as motor, tactile, or visual feedback. A typical system has three parts: a signal acquisition module, a signal processing module, and controllable external devices.\u003c\/p\u003e\n\u003ch3\u003eWhat conditions are BCIs being tested for?\u003c\/h3\u003e\n\u003cp\u003eBCI research covers movement disorders such as Parkinson's disease, spinal cord injury, and stroke; communication loss, including locked-in syndrome; and psychiatric conditions such as autism, depression, ADHD, and bipolar disorder. Uses include disease assessment, functional assistance, rehabilitation training, and neuromodulation. Most BCI applications remain in experimental or early clinical stages, and clinical availability varies by condition and region.\u003c\/p\u003e\n\u003ch3\u003eWhat are the risks or downsides of BCI?\u003c\/h3\u003e\n\u003cp\u003eInvasive BCI systems achieve the highest signal-to-noise ratio but require implantation surgery and may trigger an inflammatory response at the implantation site. Non-invasive systems are safer and less expensive, but their signal-to-noise ratio and spatial resolution are comparatively poor, and signals can be distorted by external signals and movement. Long-term electrode biocompatibility and signal resolution still need improvement.\u003c\/p\u003e\n\u003ch3\u003eHow do the different types of BCI compare?\u003c\/h3\u003e\n\u003cp\u003eNon-invasive BCIs, such as EEG-based systems, are the safest, most secure, and least expensive, with no surgery, but poorer signal quality. Invasive BCIs have the highest signal-to-noise ratio but need surgery and may cause inflammation. Semi-invasive BCIs sit between the two: electrodes are placed inside the skull but on the brain surface, without penetrating brain tissue, giving better spatial resolution than EEG with lower immunological risk than fully implanted electrodes.\u003c\/p\u003e\n\u003ch3\u003eCan a BCI help someone who cannot speak or move?\u003c\/h3\u003e\n\u003cp\u003eBCI technology offers alternative communication methods for patients who cannot speak or move, including those with locked-in syndrome, a condition of full awareness but complete paralysis. The P300 speller paradigm, developed in 1988, allowed individuals with severe motor impairments to communicate by identifying selected letters on a screen. BCI systems can also decode speech-related neural signals, enabling communication through brain activity rather than physical movement.\u003c\/p\u003e\n\u003ch3\u003eWhat is the difference between open-loop and closed-loop BCI?\u003c\/h3\u003e\n\u003cp\u003eOpen-loop BCIs analyze brain states in real time to control external devices but lack bidirectional signal flow, limiting adaptive adjustment. Closed-loop BCIs integrate real-time feedback to modulate stimulation parameters or decoding strategies. For example, adaptive deep brain stimulation dynamically adjusts stimulation intensity based on subthalamic nucleus local field potentials in Parkinson's disease patients, reducing motor fluctuations. Closed-loop systems show higher decoding accuracy but need more complex hardware.\u003c\/p\u003e\n\u003ch3\u003eWhat new BCI technologies are being developed?\u003c\/h3\u003e\n\u003cp\u003eResearch on new electrode materials is ongoing, including carbon nanomaterials and composites. Emerging BCI technologies include endovascular BCI and optogenetics BCI. The integration of multimodal technologies and artificial intelligence in BCI systems is expected to enhance performance and enable more personalized treatment. These approaches aim to address current limitations such as long-term electrode safety, signal resolution, portability, and personalized decoding.\u003c\/p\u003e\n\u003ch3\u003eWhen should a patient considering a brain–computer interface for a movement disorder, communication loss, or a psychiatric condition seek a second opinion?\u003c\/h3\u003e\n\u003cp\u003eMost BCI applications remain experimental or in early clinical stages, and availability varies by condition and region. A second opinion is reasonable before committing to an invasive or semi-invasive implant, since these require surgery and may trigger an inflammatory response at the implantation site, while non-invasive systems are safer but have poorer signal quality. Because treatment methods overlap across movement disorders, communication barriers, and psychiatric conditions such as autism and depression, an independent review can clarify which approach fits. Diagnostic Detectives Network provides independent expert second opinions.\u003c\/p\u003e\n\u003c!-- ddn:faq:end --\u003e\n\n\u003ch2 id=\"source\"\u003eSource Information\u003c\/h2\u003e\n\n\u003cp\u003e\u003cstrong\u003eOriginal article title:\u003c\/strong\u003e Brain-Computer Interfaces: The Dawn of a New Era in Disease Treatment.\u003c\/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthors:\u003c\/strong\u003e Feng Y, Zhang W, Chen J, Wu H, Wu L, Qiu Y, Deng X, Zhu C, Chen Y, Zhao Z, Wang C, Zhang X.\u003c\/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAffiliations:\u003c\/strong\u003e Faculty of Anesthesiology, Changhai Hospital, Naval Medical University, Shanghai, China; Basic Medical University, Naval Medical University, Shanghai, China; School of Anesthesiology, Naval Medical University, Shanghai, China; Beijing Institute of Basic Medical Sciences, Beijing, China; Fujian Key Laboratory of Toxicant and Drug Toxicology, Medical College, Ningde Normal University, Ningde, China; Department of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China; Department of Otolaryngology, Naval Medical Center of PLA, Navy Medical University, Shanghai, China\u003c\/p\u003e\n\n\u003cp\u003e\u003cstrong\u003ePublication:\u003c\/strong\u003e Exploration, 2026; 6:20250452\u003c\/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eDOI:\u003c\/strong\u003e https:\/\/doi.org\/10.1002\/EXP.20250452\u003c\/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eReceived:\u003c\/strong\u003e 10 July 2025 | \u003cstrong\u003eRevised:\u003c\/strong\u003e 10 October 2025 | \u003cstrong\u003eAccepted:\u003c\/strong\u003e 17 December 2025\u003c\/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eKeywords:\u003c\/strong\u003e brain–computer interface, communication barriers, electrode materials, movement disorders, neuropsychiatric disorders, psychiatric disorders\u003c\/p\u003e\n\n\u003cp\u003e\u003cem\u003eThis patient-friendly article is based on peer-reviewed research.\u003c\/em\u003e\u003c\/p\u003e","brand":"DiagnosticDetectives.Com","offers":[{"title":"Default Title","offer_id":47797565522076,"sku":null,"price":0.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0599\/5449\/5644\/files\/ddn-medical-article-brain-computer-interfaces-a-new-era-in-treating-brain-and-mental-health-disorders-hero.png?v=1791955240","url":"https:\/\/diagnosticdetectives.com\/products\/brain-computer-interfaces-a-new-era-in-treating-brain-and-mental-health-disorders","provider":"DiagnosticDetectives.Com","version":"1.0","type":"link"}