The present review highlights current research in the BCI field based on AI, which has grown rapidly over the last 15 years (76-78). The combination of BCIs and AI offers a powerful way to investigate brain function by providing direct knowledge and control of neurons controlling behavior, which will help scientists know more about the human brain and promote developments in rehabilitation medicine (8). One of the biggest advantages machine learning may confer on BCI is the ability to achieve real-time or near-real-time modulation of training parameters and subsequent adjustments in response to active real-time feedback (46). Furthermore, algorithms learn from previous data and guide users towards decisions on the basis of what they have done in the past (79). Patients and healthy subjects alike often show large variability, or even inability, of brain self-regulation for BCI control, known as BCI illiteracy (79). Adaptive machine learning methods can help participants who suffer from BCI illiteracy to gain control of the system, combining supervised techniques and unsupervised adaptation (80).
Despite the reported successes and breakthroughs in this field, there still exist some problems. First, most studies have focused on the recovery of motor ability, and the use of BCIs and AI for cognitive training is still at a very early stage (81). Second, clinical BCI applications are still very limited, and some important issues need to be solved before BCIs could be considered effective systems for rehabilitation in clinical settings. For example, stimulating electrodes with smaller diameters are needed (82). Third, machine learning algorithms learn to analyze data by generating algorithms that can rarely be predicted and comprehended in the real world, which leads to problems of unknown process between a person’s thoughts and the technology acting on their behalf (83).
As technologies that directly integrate the brain with computers become unprecedentedly complex, various ethical and social challenges that merit further examination and discussion will also arise. For instance, some forms of BCIs are likely to be expensive, posing questions of affordability and feasibility for people with severe disabilities to access them as assistive technology (84). In addition, BCIs with a decision-making device into human’s brain with AI software that autonomously adapts its operations raise questions about human autonomy by (85). Brain information as digitally stored neural data can also be exploited by others with sufficient computational power to make inferences about our memory, intentions, conscious and unconscious interests, and emotional reactions (86). Moreover, reports have surfaced about a minority of people who undergo deep-brain stimulation for Parkinson’s disease becoming hypersexual, or developing other impulse-control issues (85,86).
In conclusion, BCI based on AI is a rapidly advancing field of interdisciplinary integration of medicine, neuroscience, and engineering. Although most studies that have evaluated AI applications in BCIs to date have not been vigorously validated for reproducibility and generalizability, the goal of these devices is to improve the level of function and quality of life for people with paralysis, spinal cord injury, amputation, acquired blindness, deafness, memory deficits, and other neurological disorders. The capability to enhance normal motor, sensory or cognitive function is also emerging and will require careful regulation and control. Further technical development of BCIs, clinical trials and regulatory approval will be required before there is widespread introduction of these devices into clinical practice. The development of this technology must trigger a revolution in medicine.
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