Post by Al-Hassan RJ on Mar 25, 2014 22:10:28 GMT 5.5
Understanding the Brain-Computer Interface
The human brain has fascinated scientists since centuries, and numerous studies have tried to unravel its mysteries. Recent research has brought man and machine closer. The brain-computer interfaces in use today have a range of applications that promise to enhance our lives in ways we haven’t even imagined yet.
Vishnu Gautam
The human brain is one of the most complex structures in the universe. Several experiments have been carried out to study it in a systematic and proper way. All these experiments have had their own limitations and unique results. It was Richard Caton who first studied the electrical activity of brains in rabbits and monkeys. Then, in 1924, Han Berger invented one of the most important machines of the 20th century—the EEG or the electro-encephalograph—which is used to graphically measure the brain’s waves. Now researchers have been able to make an interface between a computer and the brain, based on the latter’s electrical activity. Although earlier devices were very slow, now, due to the availability of high-speed electronics and efficient algorithms, various highly efficient BCI (brain computer interfaces) have been developed.
Fig. 1: A neuron
Fig. 2: Converting sound waves into electric signals
Future BCI will have the capability to change our perceptions of machines and create a very strong symbiotic relationship between humans and machines. Kevin Warwick, the first cyborg (cybernetic organism) and a British professor, has been successful in developing a unique bond between himself and his machines. Not stopping at this point, he has also developed telepathy chips that use RF signals and the signals from our nervous system. The BCI has been a boon for patients who are paralysed, giving them a chance to express themselves. Although BCI technology is advancing at a fast pace, its basic concept has remained the same since the early 1970s. In order to understand the concept of BCI, one must understand the electrical activity in the human brain.
The fundamental unit
Neurons are cells that have two ramifications: an axon and a dendrite (Fig. 1).
The axon of one neuron is in contact with the dendrite of the other by certain contact synapses. A neuron has thousands of synapses. Neurons can be polar, unipolar, bipolar, multi-polar, or pyramidal in nature, depending upon the dendrite structure. When the senses receive stimuli like sound, light or aroma, that information is coded and carried to the brain by neuron chains.
Neurons communicate through an electro-chemical process. When a signal reaches a synapse, it triggers the release of neurotransmitters, which are small molecules that diffuse in intercellular space, and this activates the receptors on other synapses. In turn, the activated receptor generates electrical signals of variable intensities. The signal coming from every synapse of a neuron passively converges to the base of its axon and then they are summed up. If the resulting signal is intense enough, the axon actively propagates the signal further; otherwise, it does not.
Thus, neurons use electricity to communicate with each other. Now imagine millions of neurons sending signals at the same time. This condition produces enormous amounts of electrical activity in the brain, commonly known as the brain-wave pattern because of its cyclic wave-like nature. These brain waves emit very small pulses of varied frequencies. Basically, we can divide brain waves into four categories: Delta, Theta, Alpha and Beta. Each category reflects a different state of a person’s mind.
According to Noroma solutions oy., these waves have the frequency range shown in the Table (on next page).
Fig. 3: Placement of electrodes
Besides, there are Lambda waves that represent visual attention, and Gamma waves in the range of 25-100 Hz. We also have Mu waves in the range of 8-13 Hz, found in the motor cortex, and their amplitude fluctuates based on physical movement or intent to move. Early BCI experiments, aimed at moving the cursors on a computer screen, exploited the Beta and Mu waves.
The BCI system
Overall, a BCI has four main components:
1. A signal capture system
2. A signal processing system
3. A pattern recognition system
4. A device control system
Let us look at the example of moving the mouse cursor. The signal capture system makes use of scalp electrodes (in older systems), together with a sticky gel, to obtain signal. As these electrodes capture the electrical activity (here, capturing means that some of the current passing through the axon escapes out), the signal passes through a unity gain amplifier to boost its level for processing. As these signals are impulses at discrete frequencies, we can use Fast Fourier transform algorithms to convert them to a discrete signal (basically an A/D operation). Then, in the machine or computer, DSP software is used for proper sequence generation, after which its output is fed to a pattern recognition system to select the proper pattern or symbol. In our case, it is the pointer; and then the control device produces the operation as per the signal processing and pattern recognition results. So, we have the sequence as shown in Fig. 2.
These brain waves or signals are generated due to the differences in electrical properties carried by ions on the membrane of each neuron. The problem is that the signals are mostly blocked while only a few escape—depending on our level of concentration. More concentration means more alertness (to the task), which increases the Beta waves that can be easily detected.
If a person has hearing problem in one ear, it could be because the sensor is damaged. But since the auditory nerves are operating, a device called the cochlear implant is inserted into the ear to process sound waves, converting them to electrical signals that are then passed via the auditory nerves.
Placement of electrodes in the signal capture system
For placement of electrodes, the 10-20 system is used, which is an internationally recognised method that describes the exact location of the scalp electrodes. If we use fewer electrodes, then the distance between adjacent electrodes is 20 per cent of the total front-back or right-left distance of the skull. If we use more electrodes, then this distance is 10 per cent of the total front-back or right-left distance of the skull.
In Fig. 3, A1 and Az represent the ears’ positions. There are no central electrodes; C3, Cz and C4 are marked just for geometrical indication.
But now all of this is outdated. Due to limited portability and increased interference, electrodes are no longer used for commercial applications. Currently, the method to study the electrical activity in the brain is ‘Emotive EPOC,’ which makes use of a wireless headset. It can track conscious as well as unconscious and emotional behaviour. EPOC marks the beginning of an important era. It is highly precise and gives fast response.
Fig. 4: Wireless headsets
Applications of BCI
1. The BCI can be applied not only to brain-related products but also to bionic devices. These include bionic leg, bionic eye, bionic arm, bionic spinal cord, etc. All of these are based on the same concept of neuron electrical activity to give people a new lease of life.
2. The BCI has revolutionised the gaming experience too. There are a good number of BCI-based gaming options available in the market. Normally, the ‘conversation process’ through the BCI is not very efficient, but recent research has resulted in new methods that are available commercially, although at higher prices.
3. The concept of virtual reality has fired the imagination of many in the BCI field, as shown in the film ‘Surrogates’ starring Bruce Willis.
4. There is also the concept of ‘brain painting’ though its efficiency, as of now, is not very high.
5. The most important applications are expected to be in relation to cloud computing. What if, through BCI, we are constantly connected to the Internet via the cloud? This can change the world in more ways than can be imagined today.
The human brain has fascinated scientists since centuries, and numerous studies have tried to unravel its mysteries. Recent research has brought man and machine closer. The brain-computer interfaces in use today have a range of applications that promise to enhance our lives in ways we haven’t even imagined yet.
Vishnu Gautam
The human brain is one of the most complex structures in the universe. Several experiments have been carried out to study it in a systematic and proper way. All these experiments have had their own limitations and unique results. It was Richard Caton who first studied the electrical activity of brains in rabbits and monkeys. Then, in 1924, Han Berger invented one of the most important machines of the 20th century—the EEG or the electro-encephalograph—which is used to graphically measure the brain’s waves. Now researchers have been able to make an interface between a computer and the brain, based on the latter’s electrical activity. Although earlier devices were very slow, now, due to the availability of high-speed electronics and efficient algorithms, various highly efficient BCI (brain computer interfaces) have been developed.
Fig. 1: A neuron
Fig. 2: Converting sound waves into electric signals
Future BCI will have the capability to change our perceptions of machines and create a very strong symbiotic relationship between humans and machines. Kevin Warwick, the first cyborg (cybernetic organism) and a British professor, has been successful in developing a unique bond between himself and his machines. Not stopping at this point, he has also developed telepathy chips that use RF signals and the signals from our nervous system. The BCI has been a boon for patients who are paralysed, giving them a chance to express themselves. Although BCI technology is advancing at a fast pace, its basic concept has remained the same since the early 1970s. In order to understand the concept of BCI, one must understand the electrical activity in the human brain.
The fundamental unit
Neurons are cells that have two ramifications: an axon and a dendrite (Fig. 1).
The axon of one neuron is in contact with the dendrite of the other by certain contact synapses. A neuron has thousands of synapses. Neurons can be polar, unipolar, bipolar, multi-polar, or pyramidal in nature, depending upon the dendrite structure. When the senses receive stimuli like sound, light or aroma, that information is coded and carried to the brain by neuron chains.
Neurons communicate through an electro-chemical process. When a signal reaches a synapse, it triggers the release of neurotransmitters, which are small molecules that diffuse in intercellular space, and this activates the receptors on other synapses. In turn, the activated receptor generates electrical signals of variable intensities. The signal coming from every synapse of a neuron passively converges to the base of its axon and then they are summed up. If the resulting signal is intense enough, the axon actively propagates the signal further; otherwise, it does not.
Thus, neurons use electricity to communicate with each other. Now imagine millions of neurons sending signals at the same time. This condition produces enormous amounts of electrical activity in the brain, commonly known as the brain-wave pattern because of its cyclic wave-like nature. These brain waves emit very small pulses of varied frequencies. Basically, we can divide brain waves into four categories: Delta, Theta, Alpha and Beta. Each category reflects a different state of a person’s mind.
According to Noroma solutions oy., these waves have the frequency range shown in the Table (on next page).
Fig. 3: Placement of electrodes
Besides, there are Lambda waves that represent visual attention, and Gamma waves in the range of 25-100 Hz. We also have Mu waves in the range of 8-13 Hz, found in the motor cortex, and their amplitude fluctuates based on physical movement or intent to move. Early BCI experiments, aimed at moving the cursors on a computer screen, exploited the Beta and Mu waves.
The BCI system
Overall, a BCI has four main components:
1. A signal capture system
2. A signal processing system
3. A pattern recognition system
4. A device control system
Let us look at the example of moving the mouse cursor. The signal capture system makes use of scalp electrodes (in older systems), together with a sticky gel, to obtain signal. As these electrodes capture the electrical activity (here, capturing means that some of the current passing through the axon escapes out), the signal passes through a unity gain amplifier to boost its level for processing. As these signals are impulses at discrete frequencies, we can use Fast Fourier transform algorithms to convert them to a discrete signal (basically an A/D operation). Then, in the machine or computer, DSP software is used for proper sequence generation, after which its output is fed to a pattern recognition system to select the proper pattern or symbol. In our case, it is the pointer; and then the control device produces the operation as per the signal processing and pattern recognition results. So, we have the sequence as shown in Fig. 2.
These brain waves or signals are generated due to the differences in electrical properties carried by ions on the membrane of each neuron. The problem is that the signals are mostly blocked while only a few escape—depending on our level of concentration. More concentration means more alertness (to the task), which increases the Beta waves that can be easily detected.
If a person has hearing problem in one ear, it could be because the sensor is damaged. But since the auditory nerves are operating, a device called the cochlear implant is inserted into the ear to process sound waves, converting them to electrical signals that are then passed via the auditory nerves.
Placement of electrodes in the signal capture system
For placement of electrodes, the 10-20 system is used, which is an internationally recognised method that describes the exact location of the scalp electrodes. If we use fewer electrodes, then the distance between adjacent electrodes is 20 per cent of the total front-back or right-left distance of the skull. If we use more electrodes, then this distance is 10 per cent of the total front-back or right-left distance of the skull.
In Fig. 3, A1 and Az represent the ears’ positions. There are no central electrodes; C3, Cz and C4 are marked just for geometrical indication.
But now all of this is outdated. Due to limited portability and increased interference, electrodes are no longer used for commercial applications. Currently, the method to study the electrical activity in the brain is ‘Emotive EPOC,’ which makes use of a wireless headset. It can track conscious as well as unconscious and emotional behaviour. EPOC marks the beginning of an important era. It is highly precise and gives fast response.
Fig. 4: Wireless headsets
Applications of BCI
1. The BCI can be applied not only to brain-related products but also to bionic devices. These include bionic leg, bionic eye, bionic arm, bionic spinal cord, etc. All of these are based on the same concept of neuron electrical activity to give people a new lease of life.
2. The BCI has revolutionised the gaming experience too. There are a good number of BCI-based gaming options available in the market. Normally, the ‘conversation process’ through the BCI is not very efficient, but recent research has resulted in new methods that are available commercially, although at higher prices.
3. The concept of virtual reality has fired the imagination of many in the BCI field, as shown in the film ‘Surrogates’ starring Bruce Willis.
4. There is also the concept of ‘brain painting’ though its efficiency, as of now, is not very high.
5. The most important applications are expected to be in relation to cloud computing. What if, through BCI, we are constantly connected to the Internet via the cloud? This can change the world in more ways than can be imagined today.