The Brain Can Be Used as a Machine

The Brain Can Be Used as a Machine

The brain is the most complex structure in the body. In neurological aspects, knowledge about the brain itself and how it controls the whole body remain a mystery at some points. However, in today’s world brain activity can be used to control computers or external devices mostly prosthetics. This study field is called as ‘Brain-computer interface’ or shortly BCI.

BCI is a relatively new subject. The main problem in the field is the signal quality and extraction of the signal also, corporation of the signal with the user. The most known used signal in the BCI is electroencephalography (EEG) (Ujwal Chaudhary et al., 2021). Despite that, signal neural unit activity (SUA), multiple neural unit activity (MUA), local field potentials (LFP) and electrocorticographic arrays (ECoG) are other used signals in the area (Ujwal Chaudhary et al., 2021).

Amyotrophic lateral sclerosis (ALS) is a motor neuron disease that affects the movement and the strength of the body, loss abilities to eat (U. Chaudhary et al., 2015), speaking and breathing. In general, ALS patients have died after 3-5 years undergo the disease and there is no cure. Stroke is another disease that is caused by the interruption between vessels and the brain tissues (Ujwal Chaudhary et al., 2021; Ramos-Murguialday et al., 2013). Patients which are undergoing stroke, face some symptoms such as loss of muscle movement, lack of speaking ability, loss of memory and behaviour change. BCI is a good opportunity who undergo stroke and ALS when there is no cure. It can be used to improve these patient’s life (Benabid et al., 2019; Ujwal Chaudhary et al., 2021; Gordleeva et al., 2020; Ramos-Murguialday et al., 2013).

Stephen Hawking who undergo ALS and was able to communicate through the computer. In the general system by using some parameters directly or indirectly signals are taken from the patients and run to a computer algorithm that tries to find overlapping in the signals and give an output (Ujwal Chaudhary et al., 2021). However, in this step important question is the condition of the patient which is the locked-in state (LIS) or complete locked-in state (CLIS) (Ujwal Chaudhary et al., 2021). The difference between these conditions, CLIS patients completely lost all motor control and later stage of diagnosis. LIS patients still have a loss of control movement while some voluntary movements still occur.

Figure 1: General mechanism of the BCI system for ALS patients.

Figure 1: General mechanism of the BCI system for ALS patients.

In 2013, scientists have tried to evaluate the efficacy of BCI in stroke patients (Ramos-Murguialday et al., 2013). Two groups are separated from 32 total patients which are undergoing a chronic stroke. Each group took BMI or BCI training. BMI training was conducted according to the EEG. EEG is taken from the patients as input and analyzed by a computer program or algorithm and turn into an output which is orthosis movement. Orthosis movement can occur due to brain activity with the help of wearable equipment which is used in the paralytic part of the body (Ramos-Murguialday et al., 2013). In the first group, this training occurred the same with the program output while other group orthosis movements were conducted independently of the outcome. Results showed that orthosis movement, which is the same as output in BCI training, shows improvement in the movement of paralyzed part of the body (Ramos-Murguialday et al., 2013). The result is supported with the other brain signaling analyzes such as EMG and fMRI which are taken while the training happens (Ramos-Murguialday et al., 2013).

Figure 2: Orthosis movement-based system.

Figure 2: Orthosis movement-based system.

Figure 3: Hand orthosis equipment.

Figure 3: Hand orthosis equipment.

In 2019, another study related to BCI show valuable results for the tetraplegia patient who cannot move the lower part of the body due to injury on C4-C5 spinal cords (Benabid et al., 2019). In this study, ECoG signals are used as input for BCI. For helping the movement, exoskeleton equipment is also used and the equipment is linked to the body from wrists and hip. This equipment is controlled by the ECoG signals. To train the BCI system first MEG (Magnetoencephalography- neuroimaging technique) and fMRI are observed which are taken while the patient made a real or virtual move (Benabid et al., 2019). Secondly, ECoG data is collected and both observations run in a model learning computer system. After training the system and fixed the model, with the help of the exoskeleton and mental task of movement (which is make ECoG data) movement is occurred (Benabid et al., 2019). Despite this study, only for lower limb exoskeleton can be used, again using EMG signal which is derived from the exoskeleton by electrodes during a real or virtual move (thinking of move) (Gordleeva et al., 2020).

Figure 4: Full body exoskeleton used in BCI.

Figure 4: Full body exoskeleton used in BCI.

Figure 5: Lower Limb exoskeleton.

Figure 5: Lower Limb exoskeleton.

Figure 6: BCI with exoskeleton action mechanism

Figure 6: BCI with exoskeleton action mechanism

BCI system conduct generally 4 steps (U. Chaudhary et al., 2015). The first step is taking the signal from the patients. The second step converting the signal to useful data by conducting an algorithm in computer systems. Then, training both the computer and the patient by applying the virtual or real movements. Lastly, after training is done, with the help of electrodes or other types of equipment such as implant on the head, signal taken from the patient and turn into output as a movement or yes/no answers for ALS patients. Output is conducted by the coming signal, which is taken from the patient, overlapped on the training data (Ujwal Chaudhary et al., 2021).

In a conclusion, BCI systems still need to improve a lot due to less efficiency of the signal and their conversion to useful data (Pisarchik et al., 2019). Yet, BCI derived movements’ velocity, the strength of movements and angel of movements are limited because of the complexity of the neurotransmission for the movements (Pisarchik et al., 2019). Used signals are not provided with this knowledge and in some cases, patients cannot virtual the speed-strength or angel, they can only imagine the movement. More specifically, in BCI they might raise their hand, however, they cannot arrange velocity and angle, mostly movements highly robotic and slow. The complexity of the movements and the limited source of the signal also limited the commands in BCI. In future, this system might be used in healthy people to increase living standards and movement quality. Also, in further, we can use our brains to do multitasks at the same time with help of BCI technology especially brain implanted BCIs.

References: 

  1. Benabid, A. L., Costecalde, T., Eliseyev, A., Charvet, G., Verney, A., Karakas, S., Foerster, M., Lambert, A., Morinière, B., Abroug, N., Schaeffer, M.-C., Moly, A., Sauter-Starace, F., Ratel, D., Moro, C., Torres-Martinez, N., Langar, L., Oddoux, M., Polosan, M., … Chabardes, S. (2019). An exoskeleton controlled by an epidural wireless brain–machine interface in a tetraplegic patient: a proof-of-concept demonstration. The Lancet Neurology, 18(12). https://doi.org/10.1016/S1474-4422(19)30321-7
  2. Chaudhary, U., Birbaumer, N., & Curado, M. R. (2015). Brain-Machine Interface (BMI) in paralysis. Annals of Physical and Rehabilitation Medicine, 58(1). https://doi.org/10.1016/j.rehab.2014.11.002
  3. Chaudhary, Ujwal, Mrachacz‐Kersting, N., & Birbaumer, N. (2021). Neuropsychological and neurophysiological aspects of brain‐computer‐interface (BCI) control in paralysis. The Journal of Physiology, 599(9). https://doi.org/10.1113/JP278775
  4. Gordleeva, S. Yu., Lobov, S. A., Grigorev, N. A., Savosenkov, A. O., Shamshin, M. O., Lukoyanov, M. v., Khoruzhko, M. A., & Kazantsev, V. B. (2020). Real-Time EEG–EMG Human–Machine Interface-Based Control System for a Lower-Limb Exoskeleton. IEEE Access, 8. https://doi.org/10.1109/ACCESS.2020.2991812
  5. Pisarchik, A. N., Maksimenko, V. A., & Hramov, A. E. (2019). From Novel Technology to Novel Applications: Comment on “An Integrated Brain-Machine Interface Platform With Thousands of Channels” by Elon Musk and Neuralink. Journal of Medical Internet Research, 21(10). https://doi.org/10.2196/16356
  6. Ramos-Murguialday, A., Broetz, D., Rea, M., Läer, L., Yilmaz, Ö., Brasil, F. L., Liberati, G., Curado, M. R., Garcia-Cossio, E., Vyziotis, A., Cho, W., Agostini, M., Soares, E., Soekadar, S., Caria, A., Cohen, L. G., & Birbaumer, N. (2013). Brain-machine interface in chronic stroke rehabilitation: A controlled study. Annals of Neurology, 74(1). https://doi.org/10.1002/ana.23879

Figure References:

  1. Chaudhary, U., Birbaumer, N., & Curado, M. R. (2015). Brain-Machine Interface (BMI) in paralysis. Annals of Physical and Rehabilitation Medicine, 58(1). https://doi.org/10.1016/j.rehab.2014.11.002
  2. Chaudhary, U., Birbaumer, N., & Curado, M. R. (2015). Brain-Machine Interface (BMI) in paralysis. Annals of Physical and Rehabilitation Medicine, 58(1). https://doi.org/10.1016/j.rehab.2014.11.002
  3. Ramos-Murguialday, A., Broetz, D., Rea, M., Läer, L., Yilmaz, Ö., Brasil, F. L., Liberati, G., Curado, M. R., Garcia-Cossio, E., Vyziotis, A., Cho, W., Agostini, M., Soares, E., Soekadar, S., Caria, A., Cohen, L. G., & Birbaumer, N. (2013). Brain-machine interface in chronic stroke rehabilitation: A controlled study. Annals of Neurology, 74(1). https://doi.org/10.1002/ana.23879
  4. Benabid, A. L., Costecalde, T., Eliseyev, A., Charvet, G., Verney, A., Karakas, S., Foerster, M., Lambert, A., Morinière, B., Abroug, N., Schaeffer, M.-C., Moly, A., Sauter-Starace, F., Ratel, D., Moro, C., Torres-Martinez, N., Langar, L., Oddoux, M., Polosan, M., … Chabardes, S. (2019). An exoskeleton controlled by an epidural wireless brain–machine interface in a tetraplegic patient: a proof-of-concept demonstration. The Lancet Neurology, 18(12). https://doi.org/10.1016/S1474-4422(19)30321-7
  5. Gordleeva, S. Yu., Lobov, S. A., Grigorev, N. A., Savosenkov, A. O., Shamshin, M. O., Lukoyanov, M. v., Khoruzhko, M. A., & Kazantsev, V. B. (2020). Real-Time EEG–EMG Human–Machine Interface-Based Control System for a Lower-Limb Exoskeleton. IEEE Access, 8. https://doi.org/10.1109/ACCESS.2020.2991812
  6. Gordleeva, S. Yu., Lobov, S. A., Grigorev, N. A., Savosenkov, A. O., Shamshin, M. O., Lukoyanov, M. v., Khoruzhko, M. A., & Kazantsev, V. B. (2020). Real-Time EEG–EMG Human–Machine Interface-Based Control System for a Lower-Limb Exoskeleton. IEEE Access, 8. https://doi.org/10.1109/ACCESS.2020.2991812

 

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