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First of all I'm not a medical student so I may miss some points here.

I know that EOG (electro-oculography) works on the corneo-retinal dipole. The cornea (front of eye) is slightly positively charged and the retina (back of eye) is slightly negatively charged. When I apply electrodes on the temples and ground circuit to my forehead (helps to stabilize readings and get rid of some 60Hz interference).

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So I used that(I wrote a program) to read these signals and control some thing by moving my eyes, and follow the signals change according to my eyes movement.

Now my program can read this simple electrecity signals and convert it in order to control something else.

But this doesn't make much sense, I need to make it more understandable, for example I'm wondering if there someway to read alphabets the person thought about and receive a unique signal for it, and I mean by (someway) not just through eyes maybe with brain waves or something else I don't know about it.

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I take it you want to use eye movements for data input (rather than reading people's thoughts, which would be silly to consider). It's not the greatest idea, efficiency wise, but may have its strengths in terms of convenience. From a fairly recent (2014) review of eye based HCI:

It should be noted, though, that gaze control of WIMP is noticeably slower and more error prone than control by conventional mouse and keyboard (e.g. gaze typing reaches about 20 wpm which is far from the 80 wpm by a touch typist, see Majaranta et al. 2009a).

And the 2009 paper they refer to for state of the art is "Fast Gaze Typing with an Adjustable Dwell Time". It's basically displaying a large virtual keyboard (like your phone/tablet does) and uses a specialized algorithm to reduce errors. I'm not familiar with the details of the latter, you'll want to read the paper for that.

Microsoft Research has a more recent (2017) paper using a cascading virtual keyboard and it claims improvements for the average user. The latter paper also makes me wonder if the previous research overstated its results, because

Participants were able to achieve typing speeds of 12.39 WPM on average with our cascading technique, whereas participants were able to achieve typing speeds of 10.62 WPM on average with a static dwell time approach.

I don't know what discrepancies are due to; methodology or what is being reported, I guess.

By the way, there's a HCI stack exchange where I'm sure there are more knowledgeable people on this.

As for the title of your question "Can I read what the person thinking through electrodes or something similar?". This is a somewhat different topic, and perhaps too broad to address here. Most of the results in this latter area are much more experimental in nature, i.e. farther from a practical technology right now. Wikipedia's article on thought identification is probably a resonable start.

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  • $\begingroup$ Thanks, +1, I'm was seeking for anyway to read what person thinking of not just through eyes, but maybe with brain waves too. again thanks for answer it's helpful. $\endgroup$
    – Ibrahim
    Commented Dec 4, 2017 at 4:44
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    $\begingroup$ I would add that decoding (that's the term for what you refer to) thoughts is theoretically possible. Obviously thoughts are encoded by neurons so decoding them only requires to find which ones. Technically, it depends on what you want to decode. If it's simple yes/no, that's fairly easy. There is some great work by Jack Gallant looking at more complicated stimuli. But that's MRI, an EEG doesn't have a high enough spatial resolution to decode much. Finally, if you can record neurons, with electrodes, then that's fairly easy to decode anything, providing you record from the good area. $\endgroup$
    – user17122
    Commented Dec 4, 2017 at 6:14
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    $\begingroup$ @baca: Good point and given that Ibrahim is actually interested in the 2nd half of my answer, which is hardly developed because I know very little about that, may I suggest you expand with your own answer? I did find cmu.edu/dietrich/news/news-stories/2017/june/… as apparently the state of the art. $\endgroup$
    – got trolled too much this week
    Commented Dec 4, 2017 at 7:35

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