神經回饋訓練(Neurofeedback training, NFT)，利用儀器可觀察腦部的活動和量測腦電訊號(EEG)，訊號會經過放大及處理，並作出即時回饋。一般的神經回饋訓練利用不同的頻段劃分，可分為Theta(4-8 Hz)、Alpha(8-12 Hz)、Beta(12-30 Hz)等，根據擷取不同的頻段，可訓練出不同的效果，訓練內容會是提升指定的頻段或是抑制指定的頻段。
神經回饋訓練的研究已進行了數十年，技術有一定的成熟，但大多僅使用在實驗室或是臨床研究上，而且使用的儀器及程式並不對使用者友善。如SynAmps2，是一個可量測多通道腦電訊號的儀器，受試者需戴上電極帽及打上導電凝膠，經電極線送到電腦上，使用特定程式便可把訊號顯示，或是回饋。在使用方面，受試者不能自行操作，必須要專業人士的協助，耗時較長。而且因為有線的關係，碰到電極線或是輕微的拉扯都會造成干擾，受試者在開始實驗後活動便會受到限制，也不方便中間的休息。因此一個無線的系統可以更加便利於實驗。我們使用前額作為量測地方，主要是受試者可自行佩帶，並且使用乾式電極，不會弄髒受試者。方便受試者自行在家裡進行練習，不用花費太多時間準備。
我們驗證了前額的腦電訊號與頭上的腦電訊號的一致性，找來已經完整訓練了alpha NFT的人作受試者，同時量測前額及頭上的訊號，發現在前額的alpha波振幅比頭上的要小兩倍以上，所以在設定門檻值時需要有所調整，但仍可看到它們有趨勢上的一致。另外，前額的腦電訊號容易受眼動訊號(EOG)干擾，例如眨眼會嚴重影響前額的訊號。因此在回饋訓練時，如何減少眼動是一件重要的課題。我們測試了四種不同種類的回饋，最後得知顏色變化對受試者的影響最少，並根據受試者的回覆來看，顏色變化的回饋方式最為舒適。
前額腦電訊號NFT系統雖然在精準性比不上一般的腦電訊號 NFT系統，但是它仍有趨勢上的相同，可以告知受試者是否成功發出指定的波長，達至訓練的效果。將來更可應用於有行動障礙的人或是老人在家中使用。

Neurofeedback training (NFT), using instrument can observe the brain activities and measure electroencephalogram (EEG), after the signal pass through an amplifier and process, then feedback to the participant on the time. NFT protocols have separated to different frequency band, such as Theta wave (4-8 Hz), Alpha wave (8-12 Hz), Beta (15-20 Hz) and more. According to the frequency bands, they have different outcomes. Some training process is enhancing or reducing the specific frequency band.
NFT has been studied for a few decades, it was well developed for using in laboratory or clinical used. However most of the instruments or programs are not user friendly. For example, SynAmps2, it is an instrument can measure the multi-channel EEG. The participant need to put on an electrode cap and use the conductive gel to get the signal of the brain and send to the computer by electrode wires. The signal would display at the program, or feedback. In terms of usage, participants can’t do a NFT by themselves and must help by professional. It would take long time set up. Besides, it is easy to have artifacts by touching or pulling the wires accidently. The participants would not feel convenient while the experiment, also it is not easy to go for a break time. Therefore, a wireless system would make the experiment more convenient. We choose forehead to measure EEG. It is quick and clean because it is easy to use on their own and it is using dry electrodes. Good for practicing at home and not wasting lots of time to prepare.
We found some participants had well trained at alpha NFT to verify the agreement between forehead EEG and scalp EEG. The forehead EEG amplitude is 2 times lower than scalp EEG amplitude, so different thresholds are needed. Otherwise, forehead EEG affect by electrooculogram (EOG) easily, such as eye blinking would strongly influence the forehead EEG. For this reason, we need to consider how to reduce the eye movement while the training. We had tried using four different feedbacks and find out the color feedback would be the best feedback for most of the participants. Also, participants reported that color feedback is the most comfortable of the four feedbacks.
Forehead EEG-based NFT system has not the same accurate as scalp EEG-based NFT system, but we found that they have same tendency so it is enough for practicing at home or reviewing after training. This system can apply to mobility impaired or elders NFT at home in the future.

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