Brain Machine Interfaces (BMIs) or Brain Computer Interfaces (BCIs) are computer-based systems that enable either one-way or two-way communication between a living brain and an external machine. The brain is arguably the most formidable complex far-from-equilibrium system that orchestrates information across multiple spatial-temporal scales and mediates cognition. It poses daunting scientific challenges, with numerous technological bottlenecks and open questions representing the frontiers of our understanding, which if solved, will lead to breakthroughs and ramifications beyond immediate reach. Mainstream neuroscience views it as a classical, albeit, complex electrical-chemical system; that is, of classical/non-quantum origin. This view has led to significant insights and neuro-technological revolutions, such as the development of BCIs. Current BCIs are based on brain's electromagnetic signals, for example, Electroencephalography (EEG), Magnetoencephalography (MEG), Electrocorticography (ECoG) and Functional near-infrared spectroscopy (fNIRS). These BCIs have their specific advantages, but impose several limitations and thus novel alternatives are sought for.
Recently, several groups and companies are trying to develop successful brain implants. For instance, they are working on developing a much smaller, more powerful implant that can be placed in the brain after a simple surgery which could bring control to people with paralysis. However, it is anticipated that progress in the field of brain implants has been hampered by a combination of technological and biological factors, such as the limited understanding of the long-term behaviour of implants, unreliability of devices, and biocompatibility of the implants among others. Invasive BCI requires surgery to implant electrodes under the scalp for communicating brain signals. The main advantage is to provide a more accurate reading; however, its downside includes side effects from the surgery. After the surgery, scar tissues may form which can make brain signals weaker.
In this Research Topic, we aim to investigate current states of BCI technologies with new alternatives that may improve the current technologies with different approaches preferably minimally invasive. For instance, in the current technology landscape, photonic technologies are advancing rapidly and poised to overtake many electrical technologies, due to their unique advantages, such as miniaturisation, high speed, low thermal effects, and large integration capacity that allow for high yield, volume manufacturing, and lower cost. In this Research Topic, we welcome contributions that discuss the controversial parts of current technologies, feasibility of new technologies and limitations, and potential impact of envisaged technologies if successfully implemented in the future.
We especially welcome contributions on the following subtopics:
- Review or commentary papers on the current states of BCI
- New types of signals or data acquisition from the brain
- Novel materials for brain implant chips
- Integrated photonic chips
- Ultraweak photon emission from the brain
- Deep brain stimulation with new methods
Brain Machine Interfaces (BMIs) or Brain Computer Interfaces (BCIs) are computer-based systems that enable either one-way or two-way communication between a living brain and an external machine. The brain is arguably the most formidable complex far-from-equilibrium system that orchestrates information across multiple spatial-temporal scales and mediates cognition. It poses daunting scientific challenges, with numerous technological bottlenecks and open questions representing the frontiers of our understanding, which if solved, will lead to breakthroughs and ramifications beyond immediate reach. Mainstream neuroscience views it as a classical, albeit, complex electrical-chemical system; that is, of classical/non-quantum origin. This view has led to significant insights and neuro-technological revolutions, such as the development of BCIs. Current BCIs are based on brain's electromagnetic signals, for example, Electroencephalography (EEG), Magnetoencephalography (MEG), Electrocorticography (ECoG) and Functional near-infrared spectroscopy (fNIRS). These BCIs have their specific advantages, but impose several limitations and thus novel alternatives are sought for.
Recently, several groups and companies are trying to develop successful brain implants. For instance, they are working on developing a much smaller, more powerful implant that can be placed in the brain after a simple surgery which could bring control to people with paralysis. However, it is anticipated that progress in the field of brain implants has been hampered by a combination of technological and biological factors, such as the limited understanding of the long-term behaviour of implants, unreliability of devices, and biocompatibility of the implants among others. Invasive BCI requires surgery to implant electrodes under the scalp for communicating brain signals. The main advantage is to provide a more accurate reading; however, its downside includes side effects from the surgery. After the surgery, scar tissues may form which can make brain signals weaker.
In this Research Topic, we aim to investigate current states of BCI technologies with new alternatives that may improve the current technologies with different approaches preferably minimally invasive. For instance, in the current technology landscape, photonic technologies are advancing rapidly and poised to overtake many electrical technologies, due to their unique advantages, such as miniaturisation, high speed, low thermal effects, and large integration capacity that allow for high yield, volume manufacturing, and lower cost. In this Research Topic, we welcome contributions that discuss the controversial parts of current technologies, feasibility of new technologies and limitations, and potential impact of envisaged technologies if successfully implemented in the future.
We especially welcome contributions on the following subtopics:
- Review or commentary papers on the current states of BCI
- New types of signals or data acquisition from the brain
- Novel materials for brain implant chips
- Integrated photonic chips
- Ultraweak photon emission from the brain
- Deep brain stimulation with new methods
