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With the widespread of Internet of things, varied innovative tools are required to control the nearby smart devices. In this contest, brain-computer interface (BCI) technology can empower individuals to directly control varied electronic devices such as those used in smart home/office and associative robots via their thoughts. This process requires efficient transmission of electro-corticographical (ECoG) signals from implanted electrodes inside the brain to an external receiver located outside on the scalp. However, the realization of this vision is still challenging task due to the limited reliability of adopted wireless BCI communication systems. In particular, the generated artifacts due to in-phase/quadrature (I/Q) imbalance of utilized down converter and time interleaved analog-to-digital converters (ADCs) may lead to significant interference to desired signal which affects the detection performance. In this paper, an efficient low complexity balance technique is proposed for BCI communications to mitigate the interference using the adaptive least mean square (LMS) algorithm. Performance results of conducted experiments on a near-realistic phantom human brain model are shown to validate the designed balanced BCI (BBCI) scheme compared with the existing BCI approach. Keywords: BCI communications, IoT, I/Q down converter, LMS algorithm, Time-interleaved ADCs