这装置真能通过脑电波控制想做的事？ 还是只是个想法，没精确性？# Biology - 生物学
j*e
1 楼
http://www.emotiv.com/store/hardware/epoc-bci/epoc-neuroheadset
RT 这东西要灵了，就可以用人的思维控制机器了。
RT 这东西要灵了，就可以用人的思维控制机器了。
j*e
2 楼
有做neuroscience的吗？ 给评估一下。 总觉得如果可以用这SENSOR就把脑电波收集分
析了，进行ＴＲＡＩＮＩＮＧ 就可以控制ＧＡＭＥ（或者任何ＳＯＦＴＷＡＲＥ控制
的机器），那也可以通过脑电波知道人们在想什么了？
析了，进行ＴＲＡＩＮＩＮＧ 就可以控制ＧＡＭＥ（或者任何ＳＯＦＴＷＡＲＥ控制
的机器），那也可以通过脑电波知道人们在想什么了？
a*x
3 楼
En, I've asked a big bull in the field who published similar stories. It's
actuall just an interface on which users can train. It's the same as gamer
controllers, but the input signals are fancier.
【在 j*********e 的大作中提到】
: 有做neuroscience的吗？ 给评估一下。 总觉得如果可以用这SENSOR就把脑电波收集分
: 析了，进行ＴＲＡＩＮＩＮＧ 就可以控制ＧＡＭＥ（或者任何ＳＯＦＴＷＡＲＥ控制
: 的机器），那也可以通过脑电波知道人们在想什么了？
actuall just an interface on which users can train. It's the same as gamer
controllers, but the input signals are fancier.
【在 j*********e 的大作中提到】
: 有做neuroscience的吗？ 给评估一下。 总觉得如果可以用这SENSOR就把脑电波收集分
: 析了，进行ＴＲＡＩＮＩＮＧ 就可以控制ＧＡＭＥ（或者任何ＳＯＦＴＷＡＲＥ控制
: 的机器），那也可以通过脑电波知道人们在想什么了？
D*a
4 楼
还是这个nb～
t*o
6 楼
做了好多年了吧，brain machine interface
【在 j*********e 的大作中提到】
: http://www.emotiv.com/store/hardware/epoc-bci/epoc-neuroheadset
: RT 这东西要灵了，就可以用人的思维控制机器了。
【在 j*********e 的大作中提到】
: http://www.emotiv.com/store/hardware/epoc-bci/epoc-neuroheadset
: RT 这东西要灵了，就可以用人的思维控制机器了。
s*y
7 楼
可以的，就象另外几个人说的那样，需要训练几次。
不过目前好像精度还不是很高，不能做特别精细的工作。
【在 j*********e 的大作中提到】
: http://www.emotiv.com/store/hardware/epoc-bci/epoc-neuroheadset
: RT 这东西要灵了，就可以用人的思维控制机器了。
不过目前好像精度还不是很高，不能做特别精细的工作。
【在 j*********e 的大作中提到】
: http://www.emotiv.com/store/hardware/epoc-bci/epoc-neuroheadset
: RT 这东西要灵了，就可以用人的思维控制机器了。
b*r
8 楼
这东西已经灵了起码几个月了
r*y
9 楼
It is a quite established field for both neuroscience and engineering
research now. The fundamental was paved by Apostolos P. Georgopoulos (U
Minnesota, http://www.neurosci.umn.edu/faculty/georgopoulos.html) in their papers published in early 1980's about population coding of movement direction in Monkey's motor cortex. Another breakthrough was made by Miguel Nicolelis (Duke U, http://www.nicolelislab.net/) in early 2000's -- manipulate robotic arms/effectors using multi-unit recording in mice and monkeys' motor cortex, and then John Donoghue (Brown U, http://donoghue.neuro.brown.edu/) used it in human being for paralyzed patients (as being showed in the video posted below, well, not by me :-)). Above mentioned approaches are quite accurate but all invasive which everyone of them requires implantation of sensors in the brain, which drew many suspects of risk to patients (though I believe the paralyzed patients rather take this risk for benefits like freedom at home). Another concern of this line of approaches is the inactivation of the sensors due to tissue building up around the site along time (Someone proposed 'rejuvenating' methods such as brief high-voltage impulse or quick heating to remove the build-ups, etc).
Another line of approaches are using EEG/MEG signals to control robotic arms
or cursors on the screen, which is noninvasive and quite safe. There are
many labs working on
it since the relative low-cost and more convenient setups, as being showed
in this link. A company has exhibited their 'brain-control game' -- people
compete by moving the balls towards a target on a platform in sfn for at
least three-years in a row. I don't know whether they will do it again this
year in New Orleans, but it's quite fun to play and watch. Though I doubt
whether it relies mainly on brain signals or EMG on the forehead.
There are also people using other signature EEG/MEG signal, such as P300 on
other applications of BMI (mind spelling of words, etc.). People have posted
news and studies about those many times on this board.
In my opinion, there are at least a couple of major challenges scientists
and engineers are currently fighting on in this field:
1. How do we go beyond linear coding/decoding methods we are using and make
more progress in more complicated movements (i.e. drawing spiral circles and
playing instruments) that requires non-linear coding/decoding. There are
many studies on this approach, such as Georgopoulos himself and many other
groups (i.e. Gert Pfurtscheller in Austria) but I haven't seen a real
breakthrough yet (well, there might be some progress in grasping, which I am
not familiar with).
2. How do we get better performance by using the noisy EEG/MEG signal. For 2
-D encoding, it's pretty good now, but the performance still needs
improvement for the 3-D coding.
【在 j*********e 的大作中提到】
: http://www.emotiv.com/store/hardware/epoc-bci/epoc-neuroheadset
: RT 这东西要灵了，就可以用人的思维控制机器了。
research now. The fundamental was paved by Apostolos P. Georgopoulos (U
Minnesota, http://www.neurosci.umn.edu/faculty/georgopoulos.html) in their papers published in early 1980's about population coding of movement direction in Monkey's motor cortex. Another breakthrough was made by Miguel Nicolelis (Duke U, http://www.nicolelislab.net/) in early 2000's -- manipulate robotic arms/effectors using multi-unit recording in mice and monkeys' motor cortex, and then John Donoghue (Brown U, http://donoghue.neuro.brown.edu/) used it in human being for paralyzed patients (as being showed in the video posted below, well, not by me :-)). Above mentioned approaches are quite accurate but all invasive which everyone of them requires implantation of sensors in the brain, which drew many suspects of risk to patients (though I believe the paralyzed patients rather take this risk for benefits like freedom at home). Another concern of this line of approaches is the inactivation of the sensors due to tissue building up around the site along time (Someone proposed 'rejuvenating' methods such as brief high-voltage impulse or quick heating to remove the build-ups, etc).
Another line of approaches are using EEG/MEG signals to control robotic arms
or cursors on the screen, which is noninvasive and quite safe. There are
many labs working on
it since the relative low-cost and more convenient setups, as being showed
in this link. A company has exhibited their 'brain-control game' -- people
compete by moving the balls towards a target on a platform in sfn for at
least three-years in a row. I don't know whether they will do it again this
year in New Orleans, but it's quite fun to play and watch. Though I doubt
whether it relies mainly on brain signals or EMG on the forehead.
There are also people using other signature EEG/MEG signal, such as P300 on
other applications of BMI (mind spelling of words, etc.). People have posted
news and studies about those many times on this board.
In my opinion, there are at least a couple of major challenges scientists
and engineers are currently fighting on in this field:
1. How do we go beyond linear coding/decoding methods we are using and make
more progress in more complicated movements (i.e. drawing spiral circles and
playing instruments) that requires non-linear coding/decoding. There are
many studies on this approach, such as Georgopoulos himself and many other
groups (i.e. Gert Pfurtscheller in Austria) but I haven't seen a real
breakthrough yet (well, there might be some progress in grasping, which I am
not familiar with).
2. How do we get better performance by using the noisy EEG/MEG signal. For 2
-D encoding, it's pretty good now, but the performance still needs
improvement for the 3-D coding.
【在 j*********e 的大作中提到】
: http://www.emotiv.com/store/hardware/epoc-bci/epoc-neuroheadset
: RT 这东西要灵了，就可以用人的思维控制机器了。
j*e
10 楼
很全面，看到NATURE5月发的那篇用植入电极控制ROBOT ARM的了，应该是精度还不够。
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